199 results about "Flight zone" patented technology

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention discloses a reinforcement learning unmanned aerial vehicle route planning method and system based on a safe flight corridor, and relates to the technical field of route planning. A safe flight corridor is generated through a grid map of a target scene and starting point information; calculating a plurality of initial B spline control points through observation information of the unmanned aerial vehicle; iteratively generating a new B-spline control point based on the plurality of initial B-spline control points, the key features extracted based on the safe flight corridor and the time variable through a reinforcement learning algorithm; and generating a B spline path of the unmanned aerial vehicle according to all B spline control points. According to the invention, a feasible flight area in a complex environment is abstracted as a safe flight corridor. Environment characteristics are hidden in the safe flight corridor, and common application in different complex environments can be achieved through training in one environment. A B-spline control point is iteratively generated by using a reinforcement learning algorithm, and environment change can be iteratively adapted in real time, so that the generated B-spline path better meets the actual flight demand of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance system based on a laser radar, and belongs to the technical field of path planning, and the system specifically comprises a parameter collection module which is used for collecting original point cloud data of a flight area; carrying out obstacle geometric structure analysis to generate desensitization characteristic parameters including surface curvature and spatial density distribution; the feature analysis module is used for sending the desensitization feature parameters to an edge computing node through a hierarchical encryption transmission link, and performing primary aggregation on the feature parameters of a plurality of unmanned aerial vehicles in the same region to form a region feature template; the cloud server is used for integrating obstacle features of different areas through a security fusion protocol and constructing a global obstacle feature library; the path planning module is used for acquiring update data of the global obstacle feature library through the safe synchronization channel, and generating a three-dimensional obstacle avoidance path by combining the real-time scanning features with a feature library matching result; according to the invention, multi-machine collaborative obstacle avoidance path optimization based on obstacle avoidance information sharing is realized.

The invention relates to the technical field of unmanned aerial vehicle route planning, and discloses a low-altitude route risk map construction method and system based on artificial intelligence, and the system comprises a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint judgment module, a low-altitude route map construction module, and a low-altitude route risk analysis module. And the optimal path calibration output module is used for determining a low-altitude air route flight area by acquiring a first constraint condition of low-altitude air route environment perception, extracting key feature parameters from low-altitude air route data, performing second constraint condition analysis on the low-altitude air route, intelligently planning a low-altitude air route route according to an analysis result, and constructing a low-altitude air route map. According to the method, the low-altitude air route is acquired, the constant direction line risk analysis is performed on the low-altitude air route to calibrate the optimal path, the optimal path is output to the user terminal, the path three-dimensional display is performed in the low-altitude air route map, and the execution efficiency and the safety of the flight task are improved.

The invention relates to the technical field of unmanned aerial vehicle path planning and electromagnetic environment perception, provides an unmanned aerial vehicle path planning method and system based on a multi-frequency electromagnetic map, and aims to improve the navigation capability of an unmanned aerial vehicle in a complex electromagnetic environment and improve the task execution safety and communication reliability. The method comprises the following steps of: firstly, acquiring geographic information of a target area through GMTED2010 to carry out grid division on the area, and establishing a standardized geographic coordinate system; then, forming a three-dimensional signal to interference plus noise ratio array, extracting two-dimensional slice data through a fixed frequency dimension, and establishing a geographic space mapping relation; then, the signal to interference plus noise ratio is visualized in combination with an RGB color space, a signal to interference plus noise ratio threshold value is set according to communication requirements, a non-flying area is delimited, and finally a multi-frequency signal to interference plus noise ratio feasible region map is generated. The search weight is adjusted through an optimization algorithm, so that the path can avoid a strong interference area; the flight path is optimized by deleting redundant path points, and it is ensured that the unmanned aerial vehicle has the more efficient and stable navigation capacity.

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 relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention provides a reconstruction and extension planning method and system for an underpass channel of an airport, and relates to the technical field of airport engineering planning, and the method comprises the steps: generating at least one feasible reconstruction and extension planning scheme based on the geological conditions of a target underpass channel and the non-stop construction constraint conditions of a flight area; establishing a three-dimensional finite element model considering the aircraft moving load effect, performing structural safety verification on the scheme by using the three-dimensional finite element model, and when the scheme does not pass the verification, adjusting related parameters and performing structural safety verification again until the scheme passes the verification; and when the number of the schemes is at least two, constructing a simulation model, simulating the schemes by using the simulation model, calculating the comprehensive influence degree of the schemes on the airport operation efficiency according to a simulation result, and selecting the scheme with the minimum comprehensive influence degree as a first target feasible reconstruction and extension planning scheme. The method can improve the comprehensiveness of the planning result, reduce the whole life cycle cost and potential safety hazards of the project, and improve the airport operation efficiency.

The invention provides a point cloud downsampling method applied to unmanned aerial vehicle obstacle avoidance, and aims to solve the problems of low mapping efficiency and geometric detail loss caused by large point cloud data volume and non-uniform density in environmental perception. The method comprises the following steps: preprocessing an original point cloud through a regular grid to balance density, and extracting an effective flight area of the unmanned aerial vehicle by adopting bidirectional cloth simulation filtering and combining with a flight height; multi-level geometric features including elevation gradient, curvature and roughness are calculated, and point cloud priority scores are fused and generated based on principal component analysis; voxel grids are divided according to the size of the unmanned aerial vehicle, the sampling rate of each voxel is adaptively set in combination with the priority, farthest point sampling is executed in the feature space to select representative points, and finally a simplified point cloud model is output. The method can be integrated to a mapping module of a multi-aircraft cooperative obstacle avoidance and planning system, is suitable for unmanned aerial vehicle path planning tasks in a complex three-dimensional environment, and has the characteristics of light weight, accuracy, high efficiency and the like.

The invention relates to an urban low-altitude unmanned aerial vehicle flight path optimization method and system, and relates to the technical field of unmanned aerial vehicle path optimization, and the method comprises the steps: obtaining a three-dimensional building map of a target flight region, marking the positions of a flight starting point and a flight ending point, and determining an initial flight path; constructing a fast airflow simulator, and combining with the macroscopic airflow data to obtain microscopic airflow data; calculating the path cost for executing the initial flight path based on the microscopic airflow data at the current moment in combination with the performance parameters of the target model unmanned aerial vehicle; and performing iterative optimization on the initial flight path by taking the three-dimensional building map and the target model unmanned aerial vehicle as constraints and taking the lowest path cost as a target to obtain an optimal flight path. The problems that in traditional unmanned aerial vehicle path planning, the wind direction and the wind speed on the air route are difficult to pre-judge due to the urban low-altitude complex building environment, the path is planned only from the single distance dimension, the urban low-altitude airflow difference cannot be adapted, and the flight safety of the unmanned aerial vehicle is affected are solved.

The invention belongs to the field of Internet of Vehicles, and discloses an unmanned aerial vehicle Internet of Vehicles performance optimization method combining trajectory and unloading and related equipment, and the method comprises the steps: firstly constructing a communication and load model; constructing a multi-objective optimization function in combination with multiple calculation unloading modes, and setting constraint conditions such as an unloading decision, time delay, energy consumption and a flight area by taking minimization of total time delay and total energy consumption as an objective; and the function is solved by alternately iteratively updating the flight path and calculating the unloading mode until the maximum number of iterations is reached, and an optimal result is output. According to the method, the communication and load characteristics and multiple calculation unloading modes of the unmanned aerial vehicle auxiliary vehicle networking system are comprehensively considered, and comprehensive optimization of the system performance is realized through optimization functions and constraint conditions. According to the method, the problems of track optimization and quick response processing of calculation unloading in a complex scene can be effectively solved, the service quality of vehicle users is remarkably improved, the total time delay and the total energy consumption are reduced, and an effective solution is provided for performance improvement of an unmanned aerial vehicle networking system.

The invention discloses a dynamic planning method and system for a photovoltaic multi-unmanned aerial vehicle cooperative inspection route, and belongs to the technical field of photovoltaic power station inspection, and the method comprises the steps: carrying out the three-dimensional modeling, dividing a no-fly area and a flight area, and building a multi-unmanned aerial vehicle ad hoc network communication network; analyzing parameters of the intruding aircraft based on a prediction algorithm, and predicting a future path of the intruding aircraft; the ground control center receives and shares flight state information of each unmanned aerial vehicle in real time, allocates inspection layers of different heights according to parameters of the unmanned aerial vehicles, introduces an avoidance mechanism to adjust flight parameters, generates a global optimal route based on a dynamic planning algorithm of heuristic search, and performs routing inspection on the unmanned aerial vehicles. And the safe distance model converts unmanned aerial vehicle flight limitation, environmental factors and anti-collision constraints into a cost function, and dynamically adjusts an inspection route. According to the invention, the limitation of the unmanned aerial vehicle in a complex environment can be effectively improved, the adaptability of the photovoltaic power station unmanned aerial vehicle to environment change in patrol inspection is improved, and the safety of the whole patrol inspection process is ensured while the patrol inspection efficiency is ensured.

The invention relates to the technical field of aircraft route design, in particular to a Beidou grid-based low-altitude aircraft three-dimensional route design method, which comprises the following steps of: performing airspace type subdivision processing on a target flight area in a flight space model; judging the airspace available state of the airspace type; dividing an initial route coverage space of the target low-altitude aircraft; carrying out conflict investigation on the airspace use condition in the initial route range; when the conflict checking result is a conflict-free state, performing arc fitting on the air route space trajectory of the target low-altitude aircraft; and verifying the fitted route trajectory, and if the current airspace state is changed, adjusting the fitted route trajectory to obtain a three-dimensional route design scheme. According to the method, the airline can be planned by combining airspace type division and a dynamic available state, the airline can be changed and adjusted according to the real-time airspace, and the flexibility and safety of airline planning are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a search and rescue type rescue unmanned aerial vehicle control method, which comprises the following steps of collecting meteorological data of a rescue area through a meteorological station and a satellite, integrating temperature, humidity, wind speed and terrain height data, performing data comparison and analysis, and generating a comprehensive environment data set. According to the invention, through real-time simulation and flight condition analysis feedback of the unmanned aerial vehicle in a search and rescue task, task adaptability is improved, operation flexibility is increased, potential obstacles in a search and rescue area are dynamically identified and evaluated, the unmanned aerial vehicle is allowed to adjust a flight path in real time, the collision risk is significantly reduced, and the safety of the rescue task is improved. The optimal obstacle avoidance distance and angle between the unmanned aerial vehicle and the obstacle are calculated in real time, it is ensured that the unmanned aerial vehicle can keep the highest operation efficiency in an unsafe flight area, the unmanned aerial vehicle can more quickly position victims and effectively support ground rescue actions, dependence on human resources is reduced, and the rescue effect and efficiency are improved.

The invention discloses a dynamic MOEA / D unmanned aerial vehicle flight path optimization method based on a hybrid prediction model. The method comprises the steps that an unmanned aerial vehicle is used for collecting surrounding road environment data through a carried laser radar sensor and preprocessing the surrounding road environment data; constructing a dynamic multi-target path optimization problem, carrying out modeling by using a vector objective function, constructing a feasible path diagram in a flight area, and carrying out unmanned aerial vehicle route planning modeling; an MOEA / D algorithm population is initialized; in the flight process of the unmanned aerial vehicle, continuously detecting whether environmental parameters are changed or not, and if the environment is changed, calling an unmanned aerial vehicle path optimization method based on an improved dynamic MOEA / D algorithm; and if the environment is not changed, path optimization is carried out by using a conventional MOEA / D algorithm. The method has good real-time adaptability, multi-target balance capability and robustness, and is suitable for an intelligent flight path optimization task in a complex environment.

The invention discloses an unmanned aerial vehicle building inspection route adjustment method and system based on wind field constraint and a storage medium, and the method comprises the steps: carrying out the simulation calculation of a three-dimensional wind field distribution result of an outer contour model according to the outer contour model of a to-be-detected building and the meteorological data of the geographic position of the to-be-detected building; determining external wind field wind speed data of the to-be-detected building so as to obtain coordinate points of which the wind speed is greater than the maximum wind resistance speed of the unmanned aerial vehicle in the external wind speed data; determining a limited flight area with the wind speed greater than the maximum wind-resistant speed of the unmanned aerial vehicle in the three-dimensional wind field distribution result according to the coordinate points; obtaining a target waypoint falling in the limited flight area in an initial inspection route generated by the unmanned aerial vehicle for the outer contour model; and adjusting and updating the target waypoint according to the limited flight area to obtain a target inspection route. A limited flight area is generated through wind field simulation and wind resistance threshold constraint, and the waypoint position of the limited flight area is adjusted, so that the cruising stability and safety are improved.

The invention discloses an obstruction light control method and system in a low-altitude flight environment and a lamp, and relates to the technical field of aviation safety, and the method comprises the steps: obtaining a current obstruction light parameter corresponding to each aircraft based on preset obstruction light division information in a target flight region; based on the preset flight parameter corresponding to each aircraft, the current obstacle light parameter is adjusted, and an obstacle light initial optimization parameter is obtained; obtaining obstacle light regulation and control environment information corresponding to each aircraft, and obtaining obstacle light adjustment information based on a preset obstacle light parameter corresponding rule and the obstacle light regulation and control environment information; according to the obstacle light adjustment information, the obstacle light initial optimization parameters are adjusted, and environment suitable obstacle light parameters are obtained; and controlling the corresponding obstruction lights to perform dynamic adjustment based on the environment suitable obstruction light parameters. The method has the effect of improving the control efficiency of the aviation obstruction beacon.

The invention relates to a vision-space-time multi-mode fusion detection method for linkage targets in an airport flying area, belongs to the technical field of intelligent multi-mode perception and space-time data analysis of large airports, and solves the problem that high-precision detection of tiny targets is difficult to realize in a complex environment in the prior art. Obtaining and outputting space-time diagram features; s2, constructing a linkage target semantic graph, and obtaining and outputting semantic graph features; s3, visual-space-time fusion features are generated, an optimized model is obtained through training, and fusion features are obtained through the optimized model and serve as the visual-space-time fusion features; and S4, inputting the obtained fusion features into a downstream identification module to complete target identification and classification.

The invention discloses an unmanned aerial vehicle route generation method. The method comprises the following steps: constructing a three-dimensional modeling graph based on a to-be-flied area; acquiring the position of a first object, the position of a second object and the position of a third object in the three-dimensional modeling graph; determining a line direction according to the number corresponding to the position of the first object and the number corresponding to the position of the third object, and determining a waypoint generation area according to the line direction; based on a target point on a shooting object of the second object, the position of the first object, the position of the second object and the position of the third object, determining a waypoint of an unmanned aerial vehicle route in the waypoint generation area; and generating an unmanned aerial vehicle route according to the waypoints of the unmanned aerial vehicle route. According to the technical scheme, the route of the unmanned aerial vehicle is planned, the inspection efficiency of the unmanned aerial vehicle is improved, and manpower and material resources are saved.

The invention is suitable for the technical field of aircraft obstacle avoidance, and particularly relates to an aircraft obstacle avoidance control method and system, and the method comprises the steps: delimiting a flight region of an aircraft, collecting the stereoscopic optical remote sensing data of the flight region, drawing an obstacle recognition graph, marking the position of an obstacle, and configuring the color of the obstacle. Wherein one obstacle at least corresponds to one color, calculating the height of each obstacle, and marking the height in the obstacle identification graph; and segmenting the height into a plurality of layers, and creating branch nodes and leaf nodes. By constructing the obstacle avoidance tree, the color data of all obstacles can be visually displayed, and the color image can be quickly verified, so that the flight safety is greatly improved while the normal flight of the aircraft is ensured, the suspicious obstacles on the flight path can be verified by extracting the RGB values, the flight path planning is optimized, and the flight path planning efficiency is improved. And the flight safety is further improved.

The invention provides an unmanned aerial vehicle task guidance method based on augmented reality, a storage medium and a system, and the method comprises the steps: determining a flight task of an unmanned aerial vehicle, and obtaining a corresponding flight region; determining a target route of the unmanned aerial vehicle; determining target environment information corresponding to the target route from the initial environment information, and performing target identification on the target environment information to obtain a target object passed by the unmanned aerial vehicle in the target route and a target type corresponding to the target object; determining a target operation needing to be executed on the target object by the unmanned aerial vehicle for executing the flight task and an operation type corresponding to the target operation according to the target type, and determining a task identifier of the target object according to the operation type; and performing virtual display according to the target route and the task identifier in combination with the initial environment information to obtain a target virtual display result of the unmanned aerial vehicle. According to the invention, the task identifier and the flight route are displayed virtually, so that the error rate of a pilot during task execution is reduced, and the task execution accuracy of the unmanned aerial vehicle is improved.

The invention discloses a flight route detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining identity information of a target aircraft according to an image acquired by image acquisition equipment deployed in a flight space; according to an association relationship between the identity information and a flight area, obtaining a target flight area corresponding to the target aircraft, the target flight area being an area determined according to flight plan information reported by the target aircraft and gridding information of a flight space; determining an actual flight path of the target aircraft according to electromagnetic information detected by a base station deployed in the flight space; the actual flight path of the target aircraft is detected based on the target flight area, a detection result is obtained, and the detection result is used for representing whether the flight route of the target aircraft deviates or not. According to the scheme provided by the embodiment of the invention, the accuracy of route deviation detection can be improved, and a basis is provided for the flight safety of an aircraft.

The invention relates to an unmanned aerial vehicle flight path planning comprehensive decision method and system, and the method comprises the steps: building a two-dimensional plane coordinate system for a target flight region according to the flight height of an unmanned aerial vehicle, and solving a minimum circumcircle for all obstacles in a two-dimensional plane; establishing an obstacle threat model of the unmanned aerial vehicle according to the minimum circumcircle of all obstacles in the two-dimensional plane; constructing a flight energy consumption model of the unmanned aerial vehicle according to the hovering power when the unmanned aerial vehicle hovers and the induction power when the unmanned aerial vehicle hovers; constructing a flight curvature model of the unmanned aerial vehicle according to the difference value of the direction angles between the adjacent path nodes of the unmanned aerial vehicle; establishing a target decision model of the unmanned aerial vehicle according to the obstacle threat model, the flight energy consumption model and the flight curvature model of the unmanned aerial vehicle; setting a flight starting point and a flight ending point of the unmanned aerial vehicle in a two-dimensional plane Extending path nodes of the unmanned aerial vehicle by using the target decision model from the flight starting point of the unmanned aerial vehicle; updating the parameters of the target decision model according to the obstacle threat of the unmanned aerial vehicle and the accumulated length of the current path every time the path node is expanded; and obtaining a final flight path of the unmanned aerial vehicle until the distance from the expanded path node to the flight end point of the unmanned aerial vehicle is smaller than a set threshold value. According to the method, the detail expression capability and the algorithm flexibility of route planning are effectively improved, and efficient route planning considering safety, economy and feasibility is realized in a complex two-dimensional scene.

The invention discloses a rectangular area unmanned aerial vehicle hunting method based on differential game and reinforcement learning. The our unmanned aerial vehicle cluster obtains environment state information; spatial constraint loss is calculated through a rectangular flight area limitation loss function module, time sequence consistency loss is calculated through a batch time sequence behavior constraint module, and the spatial constraint loss and the time sequence consistency loss are combined to generate a state behavior joint hidden variable fusing space and time sequence constraints. And fusing the hidden variable with the current environment state, inputting the fused hidden variable and the current environment state into a differential game value evaluation module, obtaining a future expected value based on Nash equilibrium by solving a differential game between our part and the black flight unmanned aerial vehicle, and updating decision network parameters based on the future expected value. The cluster performs the optimized hunting action and stores empirical data for continuous training. According to the method, the decision stability, the cooperation efficiency and the task success rate of unmanned aerial vehicle cluster hunting in the rectangular limited flight area are effectively improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on an elliptical tangent graph method, and the method comprises the steps: a flight environment modeling step, which is used for building an elliptical obstacle model with a building as an obstacle, the method comprises a three-dimensional flight environment model construction step for constructing a three-dimensional flight environment model based on elevation information of a flight area and distribution of an obstacle model, a three-dimensional path acquisition step for performing path search to output a flight path, and a three-dimensional path smoothing step for smoothing path points of the flight path output by the three-dimensional path acquisition step, and obtaining a flight path for unmanned aerial vehicle control. According to the method, energy consumption is taken as a core optimization target of path planning, so that the problem of comprehensive decision-making among path length, motion smoothness and energy consumption in a traditional method is effectively solved, and the task range of the unmanned aerial vehicle is remarkably extended.

An autonomous herding system using a fleet of vehicles to manage and retrieve stray animals, combining technologies from U.S. Patents US 12102060 and US 12153451. The system employs autonomous vehicles equipped with optical-LiDAR sensors, GPS, and machine-learning image recognition to detect and guide strays back to the herd. Vehicles operate in two modes: roundup, for gathering scattered animals, and herding, for maintaining cohesion and directing movement. Positioned in traditional formations (point, swing, flank, drag), they use strategic movements, visual cues, and audio signals to influence herd behavior based on flight zone and point-of-balance principles. A trail boss module coordinates vehicle actions, ensuring efficient herd control. The system includes a battery replacement station for continuous operation, automatically swapping depleted batteries. This integration streamlines livestock management, reducing labor costs while improving animal and handler safety.

The embodiment of the invention provides a planning method and device for an unmanned aerial vehicle route and a storage medium. The planning method comprises the steps of determining a view angle range of the unmanned aerial vehicle for each sub-region in a flight region; determining an actual shooting range of the unmanned aerial vehicle for each sub-region according to the view angle range; determining a surrounding rectangle where the flight area is located; determining a first center point according to the surrounding rectangle and the standby position of the unmanned aerial vehicle; determining a plurality of circular trajectories of the unmanned aerial vehicle for the flight area according to the actual shooting range, the visual angle range and the first center point; and determining a flight route of the unmanned aerial vehicle for the flight area according to the plurality of circular trajectories. The method can reasonably plan the flight route according to the terrain, effectively shortens the route distance under the condition of ensuring the coverage rate, and effectively reduces the energy consumption of the unmanned aerial vehicle.

The invention provides a data acquisition system, method and device based on a low-altitude unmanned aerial vehicle in water affair management, and relates to the technical field of intelligent water affair. The system comprises a water affair management and control platform used for acquiring position information of the wading equipment, dividing at least one flight area according to the position information of the wading equipment, generating flight routes corresponding to the flight areas, and configuring communication frequency bands uniquely corresponding to the flight areas; the unmanned aerial vehicles are in one-to-one correspondence with the flight areas, the unmanned aerial vehicles are used for flying according to the corresponding flight routes, and under the condition that the distance between the unmanned aerial vehicles and the wading equipment meets the distance requirement, a communication link is established with the wading equipment in a preset communication mode, and a data acquisition process is executed; and sending the collected data to the water affair management and control platform through the uniquely corresponding communication frequency band under the condition that the communication condition with the water affair management and control platform meets the communication requirement. The labor cost of data acquisition can be reduced, and the acquisition efficiency can be improved.