116 results about "No-fly zone" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a multi-unmanned aerial vehicle cooperation-oriented low-altitude Internet of Things secure communication method and system, and belongs to the technical field of low-altitude Internet of Things. The method comprises the following steps: constructing a multi-unmanned aerial vehicle collaborative optimization model integrating a no-fly zone, a ground obstacle zone, communication security and energy consumption constraint; aiming at a high-dimensional non-convex characteristic of the model, a solution algorithm based on a multi-agent depth deterministic strategy gradient framework is adopted, and a local observation space containing a self state, environment information and a channel state and an action space containing speed adjustment and power adjustment are designed for each unmanned aerial vehicle agent through a centralized training decentralized execution mechanism; and a multi-target reward function is defined, so that the intelligent agent autonomously learns a cooperative strategy in a complex environment through training, and an optimized unmanned aerial vehicle track and transmitting power control instruction is jointly output. According to the invention, on the premise of strictly ensuring flight safety and physical layer safety, the sum of long-term safety communication rates of the low-altitude Internet of Things is effectively improved, and the method has good dynamic adaptability and expandability.

The invention discloses a safety supervision method and system for the flight state of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle safety supervision. Flight behavior characteristics are compared with a global reference generated through federal learning, and when a malicious unmanned aerial vehicle passes authentication through a forged electronic identifier, a positioning verification mechanism is triggered in real time; illegal invasion is effectively identified and intercepted, and the problem of response delay caused by dependence on manual checking in the prior art is solved; meanwhile, the judgment standard is automatically tightened according to the safety level of the no-fly zone, the core zone adopts a stricter threshold amplitude, and the disguise unmanned aerial vehicle is forced to be exposed abnormally; the threshold value is in smooth transition during cross-regional flight, so that the misoperation risk caused by sudden change of a judgment standard in a traditional scheme is avoided; a hardware isolation area operation behavior analysis model of the unmanned aerial vehicle flight control chip is reused, and a calculation module does not need to be newly added; existing positioning base stations are dynamically dispatched to construct a verification network, and the reconstruction cost of supervision infrastructures is remarkably reduced.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

The invention provides a multi-unmanned aerial vehicle dynamic path planning system and method, and the system comprises a coordinate conversion module which is used for carrying out the bidirectional conversion of latitude and longitude coordinates and three-dimensional grid coordinates, so as to establish a unified space-time coordinate conversion reference; the map module is used for constructing a three-dimensional airspace representation model; the three-dimensional airspace representation model records position information of an obstacle and boundary range information of the temporary no-fly zone so as to provide real-time airspace information; the route module is used for delimiting an exclusive safety area for each unmanned aerial vehicle in the air; the path planning module is used for performing multi-machine path planning based on the real-time airspace information and the space-time occupancy state; the map module, the route module and the path planning module work cooperatively based on a unified space-time coordinate conversion reference. Through unified three-dimensional grid map representation and a space-time occupation management system, integrated coordination optimization of the whole process from flight plan approval to flight execution is realized, and real-time dynamic management of a large-scale multi-machine system is supported.

The embodiment of the invention provides an unmanned aerial vehicle route early warning method, system and device for a no-fly zone and a medium, and belongs to the field of unmanned aerial vehicles. The method comprises the following steps: establishing a no-fly zone spatial database, and storing electronic fence boundaries and security level attributes of each zone; the method comprises the following steps: acquiring detection data, tracking an unmanned aerial vehicle entering a no-fly zone, generating a real-time flight path, performing compliance verification on the real-time flight path by taking a reported planned route of the unmanned aerial vehicle as a reference, marking the unmanned aerial vehicle deviating from the planned route as a suspicious target, associating identity information of the unmanned aerial vehicle with pilot information, and triggering primary early warning at the same time; and for a suspicious target, carrying out spatial superposition calculation on the flight path of the suspicious target and the electronic fence of the no-fly zone, determining an intrusion risk level according to a security level attribute, and generating an intrusion event and advanced early warning information. Accurate monitoring and track traceability management and control of the unmanned aerial vehicles in key areas are realized, and the accuracy of safety prevention and control of the unmanned aerial vehicles in the areas is improved.

The invention discloses an unmanned aerial vehicle cluster cooperative strike control method based on reinforcement learning, and the method comprises the steps: firstly building a two-dimensional confrontation environment which comprises an unmanned aerial vehicle cluster, a maneuvering target and a no-fly zone constraint; and then designing an unmanned aerial vehicle-target combat geometrical relationship based on a line-of-sight distance and a line-of-sight angle change rate and an unmanned aerial vehicle cluster space-time collaborative saturation strike mechanism, describing a state space, an action space and a reward function according to a Markov decision process, and designing a distributed collaborative strike strategy training method based on reinforcement learning. And finally, inputting the local observation of the unmanned aerial vehicle into the strategy network and outputting an acceleration instruction. According to the invention, through designing a heuristic reward based on zero-control miss distance and fusing a composite reward function of collaborative strike efficiency, energy consumption and no-fly zone avoidance, battlefield situation observation is directly mapped into an acceleration control instruction of an unmanned aerial vehicle by adopting a reinforcement learning method; and efficient interception and space-time collaborative saturation strike of various maneuvering type targets are realized.

The invention discloses an unmanned aerial vehicle autonomous cruise and obstacle avoidance method and system for a dynamic no-fly zone. Relates to the technical field of unmanned aerial vehicle autonomous navigation and path planning. The method comprises the following steps: issuing and keeping a no-fly zone set static in a task initialization stage; fusing the multi-source sensing data through a layered SLAM mapping module to generate an occupied grid map; performing global path planning by adopting an improved BFS algorithm and a weighted greedy strategy based on a map, and generating a patrol sequence covering reachable points; compressing and smoothing the spliced path to generate a final cruise trajectory; and triggering local trajectory re-planning for sudden obstacles in a non-no-fly zone based on SLAM perception in flight. According to the invention, autonomous cruise and dynamic obstacle avoidance with high coverage rate and low redundancy are realized under the condition of strictly following the constraint of the static no-fly zone.

The invention discloses a low-altitude unmanned aerial vehicle path planning method and system based on deep learning. The method comprises the following steps: acquiring flight state information and environment data of an unmanned aerial vehicle in real time; task priority calculation: calculating the task priority according to the unmanned aerial vehicle state information in combination with the task emergency degree and the task type; based on collaborative decision-making of deep reinforcement learning, decision-making is carried out on task allocation and flight paths through a deep learning algorithm in combination with unmanned aerial vehicle state and environment data and task priority information; planning an optimal flight path for the unmanned aerial vehicle according to the intelligent decision comprehensive environment data; the flight path is a discrete point set path planning target which minimizes the flight time on the premise of avoiding a no-fly zone and an obstacle; and in the process of executing the path planning by the unmanned aerial vehicle, when abnormality is detected, making a decision again, and performing path planning again until the unmanned aerial vehicle finally reaches the target position point.

The invention relates to an unmanned aerial vehicle path planning method and device based on dynamic weight and a medium, and belongs to the technical field of unmanned aerial vehicle path planning, and the method comprises the following steps: carrying out the three-dimensional modeling of an unmanned aerial vehicle flight area, obtaining an environment three-dimensional model, determining an obstacle point, a starting point and a target point of the environment three-dimensional model, and dividing a no-fly area according to the obstacle point, the starting point and the target point. And then, an improved A * algorithm is adopted to plan a global initial path, the improvement is that a dynamic weight is obtained based on the starting point, the target point and the no-fly zone, and the dynamic weight is used to optimize a cost function of the A * algorithm. And training a Gaussian process regression model by using the global initial path to obtain a global path. Meanwhile, a gravitational repulsion strategy is formulated, and the unmanned aerial vehicle flies to a target point from a starting point according to the strategy and a global path. The dynamic weight can be adaptively adjusted according to dynamic factors such as the obstacle density and the starting point-end point distance, the problems of blind search and node explosion of a traditional fixed weight in an obstacle dense area are solved, and the efficiency and accuracy of path planning are improved.

This application discloses a low-altitude airspace planning method and system based on micro-terminal areas, relating to the field of low-altitude airspace planning. The method includes: generating corresponding take-off and landing areas for low-altitude take-off and landing points or fields, and generating take-off and landing hub areas for adjacent take-off and landing points or fields to construct micro-terminal areas of the low-altitude airspace; constructing risk areas, which, together with control areas and no-fly zones, constitute the restricted areas of the low-altitude flight path network; generating alternative intermediate points connecting each take-off and landing point or field based on the restricted areas; constructing an initial low-altitude flight path network connecting all points based on the data of the alternative intermediate points and the data of the micro-terminal areas; calculating the corresponding weights of each edge line in the initial low-altitude flight path network, and optimizing the initial low-altitude flight path network based on the weighted network; and removing redundant intermediate points based on the optimized network, constructing the flight path network and the entry and exit points of each micro-terminal area to generate the final low-altitude flight path network. This application provides a more reliable technical basis for low-altitude flight path network planning.

The present invention is a system and method for a UAV flight highway and management thereof, comprising: a ground control station, a server (for example a cloud server), a geographic locator communication device, a communication transmitter, and one or more UAVs. The present invention is operable to identify ground level topography and air space objects (e.g., buildings) within a region, as well as other restrictions to UAV flights (e.g., restricted flight zones), and generates within such region a UAV flight highway, that may be multi-lane and multi-layer, based upon specific latitudinal and longitudinal points. The present invention is operable to control the flight of one or more UAVs along such flight highway, along multiple-lanes thereof, wherein the UAVs may travel at different speeds in different lanes and different layers along the UAV flight highway.

The application discloses a hierarchical encryption storage method and system for a UAV. The method comprises the following steps: obtaining a dynamic security policy coded as a smart contract from a block chain network, and calling a static security policy from a local; the dynamic security policy and the static security policy both comprise a no-fly area and an execution action; obtaining a three-dimensional position of the UAV in real time, and judging whether the three-dimensional position of the UAV is in or near the no-fly area of the dynamic security policy or the static security policy; if yes, calling a pre-configured flight task type from the local, and determining a security level according to the flight task type; executing the corresponding dynamic security policy or static security policy according to the security level, and performing hierarchical data encryption storage. When the UAV enters a sensitive area, not only corresponding flight control is implemented, but also encryption of a corresponding level is triggered automatically and in real time, so that linkage protection of flight safety and data safety is realized, and data protection and flight state are ensured to be performed synchronously.

The invention discloses an intrusion detection method and device for an electronic fence, a medium and equipment, and relates to the technical field of low-altitude electronic fences. According to the method, the candidate electronic fences where the target unmanned aerial vehicle possibly falls are quickly retrieved through the plane circumscribed rectangles of all the electronic fences, then the specific relation between the target unmanned aerial vehicle and the candidate electronic fences is quickly judged through the ray method, finally height judgment is conducted, and through layer-by-layer progressive judgment, the height of the target unmanned aerial vehicle is judged. According to the method, only possibly invaded fences can be accurately judged, the detection efficiency is improved, meanwhile, space calculation is carried out through a layer-by-layer progressive 3D calculation means, intrusion detection of variable three-dimensional electronic fences can be rapidly coped with, therefore, the electronic fences in any three-dimensional form can be self-defined in a complex airspace, a plane no-fly zone is defined, and the detection efficiency is improved. And the height layer of the airspace is accurately managed, three-dimensional airspace management is realized, and the flexibility of airspace management is improved.

The invention relates to the technical field of railway engineering construction, and discloses a sky-ground fusion live-action three-dimensional modeling method and system based on domestic data and application, which are used for existing railway line transformation. According to the modeling method, a three-dimensional coarse model of a no-fly zone is constructed through a satellite image, and a high-precision point cloud is generated in combination with ground panoramic photography for supplementation; automatically extracting a building contour based on a Mesh model, and realizing monomer modeling by using a neural network trained by a roof texture sample; and developing a segmented projection algorithm to convert the BIM model to a GIS platform in a lossless manner, thereby realizing multi-source data fusion. According to the method, a multi-source data seamless fusion technology, a BIM and three-dimensional model dynamic mapping method and a fusion scheme of a whole-process localization autonomous controllable architecture are adopted, the problem that data collection of a no-fly zone is difficult can be effectively solved, and the construction design precision and efficiency of complex line positions such as Fuhuai high-speed rails are remarkably improved.

The invention discloses an unmanned aerial vehicle capturing method, device and equipment and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: carrying out the global monitoring of a target no-fly zone through employing each initial countering unmanned aerial vehicle; the flight height of each target countering unmanned aerial vehicle is controlled to be the same as the flight height of the black flight unmanned aerial vehicle, and each target countering unmanned aerial vehicle is rotated so that the distance sensor of each target countering unmanned aerial vehicle can directly face the black flight unmanned aerial vehicle; measuring the horizontal distance between each target countering unmanned aerial vehicle and the black flight unmanned aerial vehicle by using each distance sensor, and determining the real-time coordinate of the black flight unmanned aerial vehicle according to each horizontal distance and the flight height and angle of each target countering unmanned aerial vehicle; and determining a capturing unmanned aerial vehicle, and controlling the capturing unmanned aerial vehicle to launch a capturing net to capture the black flight unmanned aerial vehicle. The black flight unmanned aerial vehicle is captured by calling the multiple countering unmanned aerial vehicles, and the problems that dead angles exist in a no-fly zone and the black flight unmanned aerial vehicle is not captured in time are solved.