171 results about "No-fly zone" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention relates to the technical field of path planning, and discloses an unmanned aerial vehicle path planning method based on large-range cruise, which comprises the steps of geographic information acquisition, geographic information processing, regional discretization, starting point and target point setting, feasible path search and path optimization. Geographic information collection is comprehensive and accurate, data of terrains, obstacles, no-fly zones and the like are covered, and a foundation is laid for path planning; regional discretization fits the characteristics of an unmanned aerial vehicle, cells are divided according to the minimum turning radius and the effective range of a sensor, and flight safety and perception are guaranteed. Feasible path searching is efficient and reasonable, and the optimal path is searched by well-designed cost and heuristic functions and considering multiple factors; path optimization is effective, based on a B spline curve smoothing method, curvature and length are reduced, energy consumption is reduced, and abrasion is reduced; safety verification is added, collision and cross-zone risks can be accurately judged, and safety guarantee is enhanced.

The invention provides an unmanned aerial vehicle intrusion real-time interception method and system based on an airspace electronic fence. The method comprises the steps of firstly obtaining real-time flight monitoring data of an unmanned aerial vehicle in a target airspace, then analyzing predefined airspace electronic fence configuration information to generate a virtual boundary constraint set, and then combining three-dimensional space coordinates and the virtual boundary constraint set to judge whether the unmanned aerial vehicle invades a no-fly zone or not. When it is judged that the unmanned aerial vehicle invades the no-fly zone, a collaborative interception instruction sequence is generated, finally, the collaborative interception instruction sequence is sent to the unmanned aerial vehicle through a radio frequency communication link, the unmanned aerial vehicle is driven to execute flight path correction, and real-time interception of the unmanned aerial vehicle is completed; according to the technical scheme, the real-time performance and safety of sensitive airspace protection are improved, meanwhile, the risk of mistakenly intercepting legal aircrafts is avoided, and non-lethal precise expelling is completed on the premise of completely eradicating the crash risk.

The invention relates to an unmanned aerial vehicle flight path planning method in a complex city low-altitude environment. The method comprises the following steps: S1, acquiring geographic information data of a target city area; s2, constructing a three-dimensional grid model of a target city region according to the geographic information data; s3, extracting obstacle boundary and no-fly zone information from the three-dimensional grid model, initializing the flight path of the unmanned aerial vehicle by adopting an A * algorithm, and obtaining a preliminary flight path planning result; s4, performing global optimization on the preliminary route planning result to obtain an optimized route planning result; and S5, if the optimized flight path planning result satisfies a preset condition, outputting the optimized flight path planning result, and if the optimized flight path planning result satisfies the preset condition, returning to S4 until the preset condition is satisfied. According to the invention, safe and efficient flight of the unmanned aerial vehicle in a complex dynamic urban low-altitude environment is realized.

The invention provides an unmanned aerial vehicle traffic management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source weather forecast time series data of a target region in a future time period, and determining the models of a plurality of unmanned aerial vehicles corresponding to the target region; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; on the basis of the models of the multiple unmanned aerial vehicles and a pre-constructed database, abnormal recognition is carried out on the fused weather forecast time sequence data, and an abnormal weather recognition result is obtained; performing risk assessment on the target unmanned aerial vehicle according to an abnormal weather identification result under the condition of determining that the abnormal weather exists, and generating a no-fly zone under the condition of determining that the medium risk and / or the high risk exists; wherein the database comprises abnormal meteorological detection rules corresponding to the unmanned aerial vehicles of various models. According to the invention, the abnormal weather identification efficiency and the traffic management efficiency are improved, the rapid response to the abnormal weather is facilitated, and the flight safety of the unmanned aerial vehicle can be ensured.

The present application belongs to the technical field of co-delivery center site selection. Disclosed are an urban-end co-distribution center site selection method and system based on air-ground coordination, which are used for solving the technical problems of an existing air-ground coordination co-delivery center site selection model being insufficiently integrated with an unmanned aerial vehicle delivery technique, failing to take into consideration constraint conditions on unmanned aerial vehicles, and the solving efficiency being low. The method comprises: establishing a planned layout set of an air-ground coordination co-delivery center; taking the minimization of the overall delivery time and the minimization of the total network operation cost as objectives to construct an air-ground coordination layout planning model; setting constraint conditions of the air-ground coordination layout planning model; by means of a grey wolf population algorithm, generating an initial site selection solution set for a pre-selected point set of the co-delivery center; filtering out invalid solutions in the initial site selection solution set on the basis of the constraint conditions; and performing an individual iterative update on the filtered initial site selection solution set, and performing a fitness assessment and no-fly zone crossing determination on a new solution set obtained after each iteration, so as to obtain a final site selection scheme.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a multi-aerial vehicle cooperative path planning method, system and device in a city and a storage medium. Comprising an information acquisition module, a path generation module, a clustering updating module and an optimal judgment module. According to the method, the initial position, the target position and the obstacle information of each unmanned aerial vehicle are acquired, an urban low-altitude airspace task scene is defined, the MP-GWO algorithm is utilized to generate a multi-aerial-vehicle cooperative initial path, differential penalty coefficients are set for different types of no-fly zones, a K-means clustering mechanism is embedded into a GWO main cycle, and the multi-aerial-vehicle cooperative initial path is obtained. According to the method, parallel sub-populations are divided according to a multi-target fitness vector, each sub-population independently executes an alpha, beta and delta guide updating strategy, a triple termination judgment mechanism integrating fixed iteration times, a convergence threshold value and elite stagnation is adopted, a current optimal planning path is output, efficient optimization of the path in a complex urban environment is achieved, and the path optimization efficiency is improved. The method is suitable for urban low-altitude airspace multi-unmanned aerial vehicle cooperative tasks.

The invention provides a multi-unmanned aerial vehicle adaptive migration method for a sudden no-fly zone, and relates to the technical field of unmanned aerial vehicles. And acquiring fusion environment data through cooperative sensing of multiple unmanned aerial vehicles, processing the data by adopting a semantic boundary extraction algorithm based on deep learning, and outputting a sudden no-fly zone map and unmanned aerial vehicle safety state codes. On the basis, the unmanned aerial vehicles are shunted according to the safety state codes and the sudden no-fly zone map, real-time obstacle avoidance tracks are generated for the non-intruding unmanned aerial vehicles, and cluster collaborative separation is executed for the intruding unmanned aerial vehicles. For unmanned aerial vehicles whose tasks are interrupted due to obstacle avoidance or separation, task decomposition is realized based on the sudden no-fly zone map and space-time isolation, and continuous execution of cluster tasks is realized through an energy consumption constraint auction mechanism, so that a plurality of unmanned aerial vehicles can better cope with a complex and changeable sudden no-fly zone environment.

The invention discloses a low-altitude airspace planning method and system based on a micro-terminal area, and relates to the field of low-altitude airspace planning, and the method comprises the steps: generating a corresponding take-off and landing area for a low-altitude take-off and landing point or a take-off and landing field, and generating a take-off and landing hub area for an adjacent take-off and landing point or a take-off and landing field, so as to construct the micro-terminal area of the low-altitude airspace; constructing a risk area, and forming a restricted area of the low-altitude airline network together with the control area and the no-fly area; on the basis of the restricted area, generating alternative intermediate points connected with the take-off and landing points or the take-off and landing fields; based on the data of the alternative intermediate points and the data of the micro-terminal area, constructing an initial low-altitude airline network connected with all the points; calculating a weight corresponding to each side line in the initial low-altitude airline network, and optimizing the initial low-altitude airline network based on a weighted network; and removing redundant intermediate points based on the optimized network, constructing access points of the airline network and each micro-terminal area, and generating a final low-altitude airline network. According to the invention, a more reliable technical basis is provided for low-altitude route network planning.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention discloses an intelligent flight route planning method and system for a low-altitude logistics aircraft, and the method comprises the steps: obtaining the three-dimensional space map data of the current position of the aircraft, including the coordinate information of a building and a telegraph pole, and the boundary range of a no-fly zone; acquiring wind speed, wind direction and rainfall data of the current area from a meteorological data source, and calculating meteorological impact factors by combining aircraft performance parameters; dynamically adjusting the candidate flight routes according to the maneuvering characteristic parameters of the aircraft, and calculating the flight time, the energy consumption cost and the safety risk score of each route; meteorological changes in the flight process are monitored in real time, and if the meteorological conditions exceed a preset threshold value, the meteorological data are obtained again, and the meteorological influence factors are updated; and carrying out local optimization adjustment on the current flight route according to the updated meteorological influence factor, generating a new flight route segment and replacing the original segment.

The invention relates to the technical field of forest steppe fire emergency command, and discloses a forest steppe fire comprehensive monitoring and emergency command method and system. The method comprises the following steps: acquiring multi-source monitoring data of a satellite, an unmanned aerial vehicle and a ground sensor, and constructing a fire scene multi-dimensional feature vector through cooperative training and feature fusion; inputting the feature vectors into a physical simulation model to generate fire scene spreading speed and temperature gradient data; analyzing a terrain obstacle coefficient based on the spreading speed data and generating a helicopter no-fly zone coordinate and a passing attenuation factor; and solving and outputting a rescue unit deployment instruction through the scheduling matrix, and projecting a holographic image by using an augmented reality terminal to realize dynamic closed-loop feedback. Through deep fusion of a physical mechanism and reinforcement learning, the problem of separation of traditional fire prediction and resource scheduling is solved, intelligent decision-making of fire accurate prediction and emergency response is realized, and the accuracy and timeliness of fire prevention and control are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle cluster navigation, and discloses an unmanned aerial vehicle cluster navigation method based on man-machine interaction. The method comprises the steps that cluster task parameters and environment constraint conditions input by an operator are efficiently received through a multi-modal interaction interface, the cluster task parameters comprise a target point coordinate set and formation form identification, and the environment constraint conditions cover no-fly zone coordinates and meteorological interference levels. And the system calls a pre-stored topological relation template according to the formation form identifier to generate an initial track, completes space conflict detection in combination with the no-fly zone coordinates, and outputs a corrected track point sequence. And inputting the meteorological interference level into a dynamic anti-interference model to calculate a course correction amount, and superposing the course correction amount to a corrected track point sequence to generate an anti-interference navigation instruction set. And acquiring position data of the unmanned aerial vehicle in real time to calculate a trajectory deviation degree, triggering a manual intervention request if the trajectory deviation degree exceeds a dynamic threshold value, calling a correction instruction, fusing to generate a hybrid control strategy, and distributing the hybrid control strategy to each node of the cluster to realize reliable man-machine collaborative navigation.

The invention provides a master-slave distributed synchronous satellite navigation decoy system and a control method, and belongs to the field of satellite navigation. The system is composed of a master control station and a plurality of synchronous deception signal generators arranged in different directions. And the master control station plans a deception trajectory by using a closed-loop control principle according to modes and parameters set by a user, satellite signal parameters output by the common-view GNSS receiver and target position and speed information transmitted by the detection system, generates a satellite number distribution and power control strategy, and generates a control instruction. And the master control station controls the deception signal generator to generate and emit a navigation deception signal causing false GNSS positioning according to the flow of the GNSS navigation signal simulator. When the signal reaches a target GNSS receiver antenna, the signal is synchronized with a GNSS satellite signal received by a target so as to control and decoy an integrated navigation system carried on the target, so that the target deviates from a preset course and track, and the purpose of hiding and efficiently preventing the target from invading a no-fly zone is achieved.

The invention provides an unmanned aerial vehicle distribution center site selection method based on a fuzzy mathematical programming particle swarm algorithm, aims to complete position selection of a park distribution center under multi-target and complex constraint conditions under the background of low-altitude economy, intelligent logistics and unmanned distribution, and comprises the following steps: carrying out detailed modeling characterization on a park multi-unmanned aerial vehicle distribution center site selection problem; delivery time and operation cost targets are subjected to target reduction through fuzzy mathematical programming, solving under complex constraint conditions is facilitated, the particle swarm optimization optimizes the search process through an elitism strategy, a limited number of Pareto solutions are obtained through an epsilon-constraint method, convergence is gradually carried out to an optimal solution, and the optimal solution is obtained. The method is suitable for multi-unmanned-aerial-vehicle distribution in an unattended park, the load and cruising ability of the unmanned aerial vehicles, energy consumption, no-fly zone distribution and other scenes with complex constraint conditions are considered, and an efficient and accurate unmanned aerial vehicle distribution center site selection scheme can be provided.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, in particular to an improved sparrow searching method for three-dimensional unmanned aerial vehicle path planning. According to the method, a three-dimensional mountain terrain model is constructed through a Gaussian function superposition method, obstacles and no-fly zones are defined by adopting a cylindrical geometric model, and obstacle avoidance constraints, take-off and landing safety constraints and flight height constraints are set; a comprehensive cost function with path length, threat penalty, flight safety height and path smoothness as sub-targets is constructed, an improved sparrow search algorithm is introduced, and a sine and cosine strategy is adopted in discoverer position updating to dynamically balance horizontal exploration and vertical obstacle avoidance. A Levy flight mechanism is introduced in follower position updating so as to enhance the capability of local development and jumping out of local optimum; the method comprises the steps of population initialization, role division, iterative optimization, collision detection and an elitist retention strategy. According to the method, a safe and smooth three-dimensional flight path with the optimal comprehensive cost is output, and the method is suitable for an actual unmanned aerial vehicle navigation task.

The invention discloses a multi-unmanned aerial vehicle area coverage path planning method with no-fly zone avoidance. The method comprises the following steps: converting an input operation area boundary and longitude and latitude coordinates of a no-fly zone into a UTM plane rectangular coordinate system, carrying out safety buffer area expansion on all no-fly zones, and calculating a difference set between the operation area and the safety buffer areas of all no-fly zones to obtain a final available operation area; segmenting the final available operation region to generate a plurality of sub-regions with balanced areas; for each sub-region, generating a parallel scanning route covering the region; and each sub-region line segment generates a parallel scanning route covering the region according to a preset interval. The method can ensure that the path avoids the no-fly zone, the zone distribution is balanced, and the calculation is efficient.

The invention discloses a method for judging whether an unmanned aerial vehicle enters a flight limiting area or a no-fly area and computer equipment. The method comprises the following steps: S1, querying the flight limiting area or the no-fly area delimited by a target area on a map according to an airspace management rule; s2, respectively acquiring geohash codes, unmanned aerial vehicle model codes and unmanned aerial vehicle height codes in the whole range and boundary points of each region, synchronously generating corresponding flight limiting codes and flight forbidding codes, and storing the flight limiting codes and the flight forbidding codes in a warehouse; s3, acquiring the longitude, latitude and height codes of the real-time position of the unmanned aerial vehicle and the type code of the unmanned aerial vehicle, and generating a to-be-compared flight limiting code and a to-be-compared flight forbidding code; and S4, comparing the to-be-compared flight limiting code with the flight limiting code, comparing the to-be-compared flight forbidding code with the flight forbidding code, and judging whether the unmanned aerial vehicle enters the flight limiting area or the flight forbidding area at present. According to the method, complex algorithm calculation is preposed, high efficiency and rapidness of real-time calculation are guaranteed, the phenomenon of identification delay is avoided, and the risk of potential safety hazards in a low-altitude airspace is reduced.

The invention relates to the technical field of scheme design of an unmanned aerial vehicle automatic fly-around no-fly zone, in particular to a method and system for an unmanned aerial vehicle to automatically fly around a no-fly zone. The method comprises the following steps: identifying a mountainous area environment based on topographic features (roughness, elevation standard deviation and a continuous climbing section), and constructing a safe path skeleton; generating an initial path by adopting an A * algorithm fused with an energy consumption factor, and optimizing a climbing rate through a dynamic gradient control model (in combination with a real-time pitch angle, a roll angle and a terrain gradient); a Bezier curve is used for achieving track smoothing; and responding to the no-fly zone change based on the local re-planning strategy. By means of terrain feature recognition and dynamic constraint cooperative control, mountain area low-climbing-rate path optimization is achieved. According to the method, the problems of insufficient path physical feasibility, dynamic environment response lag and the like under complex terrains are solved, and the safety and energy efficiency of the unmanned aerial vehicle flying in a no-fly zone and a mountainous area are remarkably improved.

The invention discloses an airborne visible light image automatic splicing method, and relates to the field of unmanned aerial vehicle image processing, and the method comprises the steps: obtaining a plurality of images collected in the flight process of an unmanned aerial vehicle, and the corresponding spatial position information and attitude information; on the basis of the spatial position relation between the images and the image overlapping information, determining image pairs capable of being registered, and constructing a connection map with the images as nodes and the image pairs capable of being registered as edges; detecting whether spatial connection fracture caused by image missing exists in the atlas or not, if so, inserting a virtual node and constructing a virtual image comprising an edge region and a transition region; and further calculating geometric transformation parameters between the images, resampling all image contents to a unified coordinate system, and executing pixel-level fusion processing in an image overlapping region. According to the invention, the problem of discontinuous image splicing of the unmanned aerial vehicle due to the existence of a no-fly zone, a privacy protection zone and the like of the unmanned aerial vehicle is solved.

The invention relates to a method and a device for generating an obstacle avoidance route of an unmanned aerial vehicle, and electronic equipment, relates to the field of unmanned aerial vehicles, and solves the technical problem of how to quickly, effectively and automatically plan a new route to meet the obstacle avoidance requirement. The method for generating the obstacle avoidance route of the unmanned aerial vehicle comprises the following steps: step S10, acquiring position information of two end points of a leg passing through a no-fly zone in a planned route of the unmanned aerial vehicle; step S20, judging whether two end points of the leg passing through the no-fly zone are outside the no-fly zone, and if so, entering step S30; otherwise, determining a plurality of interconnected flight segments with two end points outside the no-fly zone from the planned route of the unmanned aerial vehicle, and entering step S30; s30, setting two end points outside the no-fly zone as a starting point and an ending point, and determining a passable area and a non-passable area according to the starting point, the ending point and the position information of each vertex of the no-fly zone; and S40, based on the passable area and the non-passable area, generating an obstacle avoidance route from the starting point to the terminal point by using a path planning algorithm.

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 provides an aerial survey unmanned aerial vehicle route planning method and system, and the method mainly comprises four steps: map coordinate conversion, polygon processing, polygon decomposition, and path planning, and through the four steps, the aerial survey unmanned aerial vehicle route planning method employs a full-coverage path planning algorithm. After the boundary of the operation area and the coordinate information of the no-fly area are obtained, the air route is automatically generated to avoid the no-fly area and the path is shortest, so that the method is really applied to aerial photogrammetry operation, the reasonable shortest path can be planned for the operation area in any shape, the operation area cannot fly to the no-fly area, the turning frequency is reduced, and the working efficiency is improved. The phenomenon of repeated flight is avoided, and the operation efficiency of the aerial survey unmanned aerial vehicle is improved.

The invention relates to the technical field of wind power plant inspection, and discloses an unmanned aerial vehicle inspection path planning method and device for a wind power plant, equipment and a medium, and the method comprises the steps: building an inspection scene of the wind power plant and an unmanned aerial vehicle no-fly zone in the inspection scene, and determining a fan inspection point based on the unmanned aerial vehicle no-fly zone; taking the minimum inspection path cost as a target, and enabling a preset unmanned aerial vehicle global inspection task to be equivalent to a traveling salesman problem without a fixed starting point based on a fan inspection point; solving a feasible inspection path and a corresponding path length in the traveling salesman problem without a fixed starting point by adopting a target offset fast search tree algorithm of strategy interaction; and solving an optimal inspection path by adopting a genetic algorithm based on the feasible inspection path and the corresponding path length. The routing inspection path can avoid mountain obstacles and no-fly zones and deviates to the target point, the routing inspection efficiency is high, and the problem that unmanned aerial vehicle routing inspection path planning is not carried out for the wind power plant scene in the mountain land is solved.

The invention discloses a three-dimensional point cloud-based intelligent management and control method and system for non-power-cut operation of a distribution network, and relates to the technical field of power operation management and control, and the method comprises the steps: integrating distribution network line data through a GIS, and eliminating a no-fly zone and an interference zone to generate an unmanned plane inspection route; a high-definition camera and a laser radar are used for collecting images and environment parameters, and a three-dimensional point cloud model is constructed. And calculating a shooting angle and classifying images based on the position relationship between the unmanned aerial vehicle and the tower, and fusing multi-source data to optimize the model precision. Effective images are screened, optimal angle parameters are determined, an optimal route is generated, the abnormal state of equipment is automatically recognized according to a route real-time collection data comparison model, fault alarm is triggered, maintenance resources are dispatched, and dynamic monitoring and closed-loop management and control are achieved. The method has the advantages that three-dimensional modeling, intelligent path planning and real-time analysis are integrated, the inspection efficiency and safety are remarkably improved, the manual intervention requirement is reduced, and the method is suitable for complex environment distribution network operation and maintenance.

The embodiment of the invention aims to provide a gridding low-altitude three-dimensional traffic light representation and calculation method and device, a computer program product and a computer program storage medium. Wherein the current three-dimensional grid position of at least one unmanned aerial vehicle is tracked in real time; and when detecting that one or more unmanned aerial vehicles trigger a conflict event at the current three-dimensional grid position, instructing the unmanned aerial vehicle triggering the conflict event to hover, and determining a new flight path for the unmanned aerial vehicle. According to the embodiment of the invention, the three-dimensional airspace grid unit configures the state of the airspace, such as a no-fly zone or a green light suitable-to-fly zone. The system tracks the current grid position of the unmanned aerial vehicle, and when the unmanned aerial vehicle triggers a conflict event, the system sends a hovering instruction to the unmanned aerial vehicle and gives a corresponding conflict solution, such as detour or sequential passage.

The invention discloses a method and a system for planning a route of an unmanned aerial vehicle over a lake by fusing meteorological data, and belongs to the technical field of unmanned aerial vehicle navigation. The method comprises the following steps: acquiring an initial route according to an unmanned aerial vehicle flight starting place and no-fly zone data; obtaining current meteorological data of an area corresponding to the initial route of the unmanned aerial vehicle, inputting the current meteorological data into the climate risk model, outputting a risk entropy value, and judging whether the unmanned aerial vehicle passes through a high-risk field; meanwhile, the flight safety of the unmanned aerial vehicle is judged according to the stability function; if the initial route passes through the high-risk field and the flight safety of the unmanned aerial vehicle is dangerous, taking a cost function to obtain a minimum value as a target, and optimizing the initial route to obtain a target route; and if the flight safety of the unmanned aerial vehicle is safe, taking the initial route as a target route. And safe flight and compliance control in a complex airspace above a lake are realized.

The invention relates to takeoff and landing field management and control, in particular to a traffic light management and control method and device suitable for a low-altitude multi-aircraft mixed takeoff and landing field, an airspace is divided into a plurality of three-dimensional grid units, the three-dimensional grid units suitable for flight are represented by green, and the three-dimensional grid units not suitable for flight are represented by red; three-dimensional grid units passed by the manned aircraft within a certain time period are prompted in a striking mode, it is determined that the corresponding airspace is a strict no-fly zone within a certain time period, and an improved fast and adaptive perception planning algorithm is adopted to plan a flight path for the unmanned aerial vehicle; according to the technical scheme, the defects that in the prior art, visual and refined expression is lacked in the aspect of airspace state management, and the requirements of a low-altitude multi-aircraft mixed take-off and landing field for flight safety and efficient operation are difficult to meet in the aspect of unmanned aerial vehicle flight path planning can be effectively overcome.

The invention belongs to the technical field of reentry flight path planning. The invention provides a no-fly zone avoidance optimal reentry flight path planning method. According to the embodiment of the invention, a variant high-speed three-degree-of-freedom motion model is established, and the variable system number is expanded into the control quantity; specific forms of process constraint, control quantity constraint, end point constraint, performance index constraint and no-fly zone constraint are given; and aiming at the constraint condition and the motion model, converting a trajectory optimization problem into a Bolaz type optimal control problem, and carrying out time domain transformation and optimal control solution on the Bolaz type optimal control problem based on an hp self-adaptive pseudo-spectral method. A no-fly zone is converted into path constraint, an hp self-adaptive pseudo-spectral method is introduced, the rapid convergence of an h method and the calculation sparsity of a p method are fully combined, and more reasonable collocation point distribution is achieved. The problem of optimal trajectory planning of the high-precision no-fly zone is solved.

The invention discloses a highway inspection unmanned aerial vehicle task planning monitoring system based on a knowledge graph, particularly relates to the technical field of unmanned aerial vehicle intelligent inspection, and is used for solving the problem that an existing system cannot generate a feasible decision scheme when an airspace control rule conflicts with an emergency timeliness requirement. The method comprises the following steps: constructing a knowledge graph integrating geographic no-fly zone space coordinates, an aging constraint time threshold and a traffic flow thermodynamic map layer, triggering a rule conflict state when detecting that an overlapping region exists between an aging constraint target position and a no-fly zone, and further fusing a real-time traffic control instruction and unmanned aerial vehicle maneuvering characteristic parameters to generate an elastic space domain; and after verification of a high-flow area overlapping rate threshold value or approval of a forced passing instruction, creating a temporary channel entity which covers the overlapping area coordinates and is associated with a time threshold value, and finally generating an inspection route which penetrates through a conflict area and meets a passing time limit based on the entity, thereby realizing coordination of dynamic rule conflict resolution and safety emergency inspection.