1087 results about "Drone flies" patented technology

The invention discloses an unmanned aerial vehicle intelligent dynamic inventory algorithm, and relates to the technical field of warehouse management, the unmanned aerial vehicle flight inventory route is planned according to warehouse layout, shelf distribution and material storage area information, and non-inventory areas are marked; the unmanned aerial vehicle flies to obtain radio frequency data and material image visual data on warehouse materials; the collected radio frequency data and the material image visual data are preprocessed, feature extraction is carried out, and radio frequency features and visual features are obtained; according to the unmanned aerial vehicle intelligent dynamic inventory algorithm, through cooperative application of radio frequency and visual technology, the inventory accuracy is enhanced, radio frequency identification can accurately read material label information and obtain key data of material numbers and category codes, and a visual identification system can be used for capturing material appearance characteristics from multiple angles and multiple distances and assisting in confirming material categories and states; large-area areas in a warehouse can be efficiently checked in a short time, and smooth operation of the warehouse is guaranteed.

The invention discloses a low-altitude patrol system and method for a smart city AI unmanned aerial vehicle, particularly relates to the technical field of unmanned aerial vehicle traffic control, and is used for solving the problem that a dynamic coupling effect caused by airflow interaction is not fully considered when an existing unmanned aerial vehicle cooperatively flies in a dense area. By monitoring the flight state and the relative position of the unmanned aerial vehicle in real time, the dense degree of the unmanned aerial vehicle in a target area is judged, and if the dense degree exceeds a safety threshold value, interaction between the unmanned aerial vehicle and surrounding airflow is analyzed through a nonlinear dynamic model, and possible dynamic coupling effect hidden dangers are evaluated; meanwhile, in combination with phase-space reconstruction and multi-scale analysis, potential interference of ground reflection airflow on the flight path of the unmanned aerial vehicle is evaluated; and whether overall traffic coordination optimization of the target area is carried out is judged, and the trajectory adjustment priority is determined according to the flight path complexity of each unmanned aerial vehicle, so that the flight path conflict is effectively avoided, and the flight safety and execution efficiency of the unmanned aerial vehicle cluster are improved.

The invention relates to the technical field of unmanned aerial vehicle management, in particular to an unmanned aerial vehicle low-altitude flight record management system. The invention aims to solve the problems that unmanned aerial vehicle low-altitude flight records are mainly used for flight data storage and simple playback in an existing unmanned aerial vehicle management system, and deep analysis and judgment of the unmanned aerial vehicle low-altitude flight records are lacked. According to the invention, the task evaluation module evaluates the feasibility of the low-altitude flight task of the unmanned aerial vehicle and generates an initial flight scheme, the path optimization module optimizes the initial flight scheme to generate a flight path adapted to a low-altitude environment, and the real-time monitoring module evaluates the flight state of the unmanned aerial vehicle in real time. The recording and judging module evaluates the flight state of the unmanned aerial vehicle after the low-altitude flight task of the unmanned aerial vehicle is completed; and the recording and playback module provides whole-course playback of the low-altitude flight process of the unmanned aerial vehicle according to low-altitude flight records of the unmanned aerial vehicle. Through mutual cooperation among the modules, comprehensive data support and application support are provided for low-altitude flight management of the unmanned aerial vehicle.

The invention discloses a flight control method and system of an unmanned aerial vehicle tracking technology, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: before an unmanned aerial vehicle takes off, starting an airborne sensor in advance, executing parameter initialization and calibration operation, and installing a magnetic marker on a tracking target; acquiring the body position of the unmanned aerial vehicle in the flight process, and sensing the target position of the tracking target in real time by adopting a multi-sensor data fusion algorithm; obstacles existing in the flight path are detected in real time, a three-dimensional environment model is integrated, and the motion state of a tracking target is captured in combination with a follow-up visual tracking algorithm; target tracking data are stored and fed back in real time, and the battery state and the energy consumption speed of the unmanned aerial vehicle are monitored. By combining the magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, high-precision target tracking can be realized in a dynamic complex environment, meanwhile, obstacles are effectively avoided, and the flight stability and tracking precision of the unmanned aerial vehicle are ensured.

The invention discloses an unmanned aerial vehicle flight path optimization system and method based on artificial intelligence and Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting communication delay, environment data and an unmanned aerial vehicle state in real time; analyzing historical communication delay data by using a Markov chain, and predicting a basic delay value in a short time in the future; correcting the basic delay value through a machine learning model; outputting a final delay prediction result; calculating a delay risk index, and judging a delay risk level; based on the delay risk level, when the delay risk level is low risk, sending a control instruction in advance according to the final delay prediction result; and when the delay risk level is medium or high risk, calling a pre-trained reinforcement learning strategy library, and generating a self-adaptive control instruction. According to the method, the multi-dimensional risk index is constructed, multiple strategies are provided to adjust the flight path according to the real-time data and the risk index, and the flight stability of the unmanned aerial vehicle is improved.

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

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

The present invention discloses a method and system for determining the flight path of a drone by object identification, and relates to the field of data encryption technology, including collecting environmental data through multiple sensors and performing preprocessing, extracting features from the preprocessed environmental data, and performing feature fusion to obtain a comprehensive feature vector, generating a three-dimensional semantic map based on the comprehensive feature vector by using a semantic segmentation technology, dividing an area into a safe area and a dangerous area, and performing object identification on obstacles in combination with the area division results, predicting the motion trajectory of dynamic obstacles, and planning the optimal flight path of the drone in combination with the object identification, semantic segmentation and motion trajectory prediction results.

The invention provides a hydrogen fuel cell hybrid power unmanned aerial vehicle energy adaptive optimization system and method. The system and method aim at achieving adaptive optimization when multiple uncertain factors exist in the flight process of an unmanned aerial vehicle. Through mechanical analysis of the typical flight profile of the unmanned aerial vehicle, an estimation model of load demand power in each stage in the flight process of the unmanned aerial vehicle is constructed. The core of the method is that the flight process of the unmanned aerial vehicle is modeled as a Markov decision process, and a self-adaptive optimization model composed of a power energy consumption state space, a power supply power distribution action space, a reward function, an electric energy supply constraint condition and the like is reasonably constructed. And for an electric energy distribution reward function, a near-end strategy optimization algorithm is applied to train an intelligent agent to continuously update an actor network and iterative learning to approach an expected control strategy, so that self-adaptive intelligent optimal distribution of the hydrogen energy and the electric energy by the hydrogen fuel hybrid power unmanned aerial vehicle energy management system is realized, and a system implementation scheme is provided.

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

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

The invention provides an unmanned aerial vehicle group path planning method and system based on environment analysis, and relates to the technical field of unmanned aerial vehicle path planning. Unmanned aerial vehicle operation data are collected, unmanned aerial vehicle operation abnormity is analyzed based on the unmanned aerial vehicle operation data, an unmanned aerial vehicle group flight path is collected, and the flight distance in the unmanned aerial vehicle flight process is obtained; the method comprises the steps of screening out a flight risk interval based on a flight interval in a flight process of an unmanned aerial vehicle, collecting weather data, analyzing flight abnormity generated by the flight risk interval under the weather influence based on the weather data, and obtaining unmanned aerial vehicle group density in a set range when the unmanned aerial vehicle generates the flight abnormity based on an unmanned aerial vehicle group flight path. The flight influence of flight abnormity on the unmanned aerial vehicle group is analyzed based on the unmanned aerial vehicle group density, the optimal flight path of the unmanned aerial vehicle group is screened out based on the flight influence of the flight abnormity on the unmanned aerial vehicle group, potential flight risks such as out-of-control, course deviation and even crash of the unmanned aerial vehicles can be predicted, and therefore the flight safety is improved.

The invention belongs to the technical field of mobile communication, and relates to an unmanned aerial vehicle adjustment method in a forest fire scene, which comprises the following steps: constructing a system model; the system model comprises an unmanned aerial vehicle model and a forest fire environment model; according to the unmanned aerial vehicle model and the forest fire environment model, constructing an optimization problem by taking minimization of energy consumption of the unmanned aerial vehicle as a target; the optimization problem is converted into a Markov decision process, the optimization problem is solved by adopting a deep reinforcement learning algorithm based on the Markov decision process, and the attitude of the unmanned aerial vehicle is obtained; according to the method, the unmanned aerial vehicle model and the forest fire environment model are constructed by comprehensively considering stroke, smoke and flame spreading in the forest fire scene, the optimization problem is constructed according to the unmanned aerial vehicle model and the forest fire environment model, and the target function of the optimization problem considers the total energy consumption of flight, hovering, data collection and transmission of the unmanned aerial vehicle. Therefore, the method adapts to the actual environment condition of forest fire, and the attitude of the unmanned aerial vehicle can be adjusted more effectively.

The invention provides an unmanned aerial vehicle route planning method and device, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: determining initial routes of a plurality of unmanned aerial vehicles according to a first constraint condition of the unmanned aerial vehicles; performing space-time grid division on each initial route to obtain a plurality of airspace grids; determining a safety distance between the unmanned aerial vehicles, wherein the safety distance is expressed as a plurality of airspace grids; and according to the second constraint condition of the unmanned aerial vehicle, determining the simultaneous flight route of the plurality of unmanned aerial vehicles. According to the invention, route planning of simultaneous flight of a large number of unmanned aerial vehicles is realized, the utilization rate of airspace is improved, and maximum utilization of route airspace resources is realized. Meanwhile, the low-altitude air traffic control system can perceive, monitor and control the flight of the unmanned aerial vehicle in real time within the defined route range, and unsafe conditions such as communication loss and out-of-control are prevented. The flight density of the unmanned aerial vehicle is improved on the premise of safe flight by finely dividing the airspace of the flight route, and airspace resources of the flight route are utilized to the maximum extent.

A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system: automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the flight plans based on the updated 3D model

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

A system for generating an environment for an operation using a set of assets includes processor(s) configured to obtain data associated with task(s) to be performed by a set of assets, wherein: 1) the set of assets comprises semi-autonomous drones and 2) the data associated with the task(s) comprises other drone flight plan(s); determine a discretized representation of the geographic location, wherein the discretized representation comprises discrete elements each corresponding to a volume associated with the geographic location; annotate the discretized representation with the other drone flight plan(s) to create an annotated representation; determine a first flight plan of one drone, wherein the first flight plan is determined based on the annotated representation; and communicate information pertaining to the first flight plan to at least one other asset.

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

The embodiment of the invention provides an unmanned aerial vehicle flight path detection method and equipment, and the method comprises the steps: firstly determining a flight plan of an unmanned aerial vehicle, then generating a flight path line and a first space-time envelope corresponding to the flight plan according to the flight plan, monitoring the flight attitude data of the unmanned aerial vehicle after the unmanned aerial vehicle takes off, and carrying out the detection of the flight path of the unmanned aerial vehicle. A second space-time envelope body can be obtained according to a first time point, a first distance between space position information of the unmanned aerial vehicle at the first time point and a flight track point in the second space-time envelope body is calculated, and whether the unmanned aerial vehicle deviates from the first space-time envelope body or not is judged according to the first distance. According to the scheme, by introducing the flight plan, the flight track line and the space-time envelope of flight, the flight path of the unmanned aerial vehicle in specific time is predicted, the full view of the flight process in the whole flight plan is mastered, meanwhile, the distance relation between the real-time position of the unmanned aerial vehicle and the corresponding space-time envelope is calculated, and the possible yaw risk of the unmanned aerial vehicle is accurately recognized.

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

The invention discloses a multi-unmanned aerial vehicle cooperative obstacle avoidance method for static and dynamic obstacles, and the method comprises the steps: transmitting the motion state and obstacle information of each unmanned aerial vehicle to surrounding unmanned aerial vehicles; calculating an obstacle avoidance controller of each unmanned aerial vehicle according to the relative position information of the obstacle and the unmanned aerial vehicle in combination with the current speed information of the unmanned aerial vehicle; designing an inter-unmanned aerial vehicle collision avoidance controller and a formation keeping controller according to the relative position information and the relative speed information between the unmanned aerial vehicles; designing a driving controller for the unmanned aerial vehicle to fly to the target area according to the relative position information between the unmanned aerial vehicle and the target point; and a cooperative obstacle avoidance controller is calculated according to the controllers and a corresponding unmanned aerial vehicle is controlled. The invention provides a multi-unmanned aerial vehicle cooperative obstacle avoidance control scheme which meets actual requirements and is easy to implement, the safety and the integrity of a multi-unmanned aerial vehicle system can be well balanced, and obstacle avoidance and formation reconstruction tasks in static and dynamic obstacle environments can be effectively completed while the multi-unmanned aerial vehicle system flies to a target area.

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

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

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

The invention provides an airspace gridding method based on flight time, electronic equipment and a storage medium, and relates to the technical field of airspace gridding, and the method comprises the steps: obtaining a flight path RE of a target unmanned plane; determining the level L of the airspace grid corresponding to the RE; obtaining each L-level target airspace grid corresponding to the RE to obtain an airspace grid list A; determining an L-level code of each target airspace grid to obtain a target airspace grid code list GL; according to the waypoint information corresponding to the RE, determining the time when the target unmanned aerial vehicle flies to each target airspace grid so as to obtain a to-be-coded time list t; encoding each time to be encoded in the t to obtain an L-level time encoding list TL; adding the TLi into the GLi to obtain a target airspace grid code containing the flight time; according to the invention, whether the airspace grid is occupied or not can be determined according to the corresponding time coding information, so that collision is avoided.

The invention discloses a geological park geological relics information acquisition method and system based on multi-source data, and the method comprises the steps: obtaining a GIS ground surface image, geological relics environment parameters and geological relics updating data, updating the flight path of an unmanned aerial vehicle, executing the flight strategy of the unmanned aerial vehicle, obtaining a low-altitude aerial image, carrying out the data matching and data correction, and obtaining the geological park geological relics information. Determining a data fusion area according to the multi-source correction data, constructing a geological relic multi-source data fusion model based on the multi-source correction data, and inputting to-be-collected geological park multi-source sensing data into the geological relic multi-source data fusion model to obtain geological relic collection information, and determining an acquisition scheme according to the data fusion area and the acquisition time. The method not only can improve the efficiency and accuracy of geological park geological relic information acquisition based on multi-source data, but also has good interpretability, and can be directly applied to a geological park geological relic information acquisition system based on multi-source data.

The invention discloses a real estate surveying and mapping area management method and system based on remote sensing images, and particularly relates to the technical field of real estate surveying and mapping, and the method comprises the steps: photographing a real estate surveying and mapping area through an unmanned aerial vehicle, and obtaining the image data of the real estate of the unmanned aerial vehicle under different flight paths and different attitudes; designing a fitness function of an unmanned aerial vehicle flight path genetic algorithm, optimizing the unmanned aerial vehicle flight path, performing optimization in combination with flight information and remote sensing image information of the unmanned aerial vehicle in different flight paths, and obtaining quality constraint coefficients of different real estate in the unmanned aerial vehicle flight path through error information and appearance information of each real estate; and setting a quality constraint coefficient threshold, and determining the optimal flight path of the unmanned aerial vehicle in the real estate surveying and mapping area based on a double-layer optimization strategy in combination with the fitness function and the quality constraint coefficient of the genetic algorithm, so that the invention provides an efficient and accurate unmanned aerial vehicle flight path planning scheme which is beneficial to the surveying and mapping precision and efficiency.

The present invention provides a UAV carrying structure and a UAV, belonging to the technical field of UAVs; the structure comprises a box body and a connecting part for hanging on the bottom of the UAV body, the box body is used to store materials, and the connecting part is used to connect with the bottom of the UAV body; through the arrangement of the connecting part, after placing the materials, the weight of the materials themselves can make the inclined surface of the wedge piece and the fixing piece tightly pressed and fitted, thereby realizing a stable and reliable connection between the box body and the connecting part, strongly ensuring that the box body will not be easily separated from the connecting part during the entire process of the UAV flight, thereby improving the safety and reliability of the entire UAV carrying structure, and when the UAV is flying, even if it is affected by external forces such as shaking, the wedge piece and the fixing piece will be further squeezed due to relative movement, so that the connection strength between the two is continuously enhanced during the flight process, and can better cope with complex flight conditions and always maintain a stable connection state.

The invention relates to the technical field of unmanned aerial vehicle data processing, in particular to an unmanned aerial vehicle data processing method and system based on embedded combination with off-line map fusion, and the method comprises the following steps: before an unmanned aerial vehicle is started, pre-loading and dividing flight area map data, and classifying and storing the map data according to geographic areas and importance; and generating a plurality of region classification map data. According to the method and the device, the map data is preloaded and classified and stored before the unmanned aerial vehicle flies, so that the autonomous navigation capability of the unmanned aerial vehicle in a complex environment is improved, especially under the condition that a network is unavailable or signals are weak. The introduction of the quadtree spatial index optimizes the indexing and retrieval process of the map data, accelerates the response time and reduces the consumption of computing resources. The ant colony algorithm is used for path optimization, the data access mode and the access frequency of the current flight area are adjusted, and the efficiency and the economical efficiency of path planning are improved.

The invention discloses an unmanned aerial vehicle flight path planning method capable of completely avoiding a no-fly zone, which belongs to the field of unmanned aerial vehicle flight path planning and comprises an application scene of the unmanned aerial vehicle flight path planning method in the no-fly zone, and the application scene of the unmanned aerial vehicle flight path planning method in the no-fly zone comprises a communication scene of an unmanned aerial vehicle and a terminal user in the no-fly zone. According to the invention, the unmanned aerial vehicle can be absolutely prevented from entering any no-fly zone in the flight process; a plurality of straight lines are adopted to approximate the actual flight path of the unmanned aerial vehicle, and the distance from the intersection point of two adjacent straight lines to the center point of the no-fly zone is calculated, so that the flight path of the unmanned aerial vehicle is strictly monitored and planned, and the accuracy of flight path planning and the flight efficiency and task execution effect of the unmanned aerial vehicle are improved; according to the method, the constraint of the adjacent time slots on the continuous trajectory is increased, and the safety and robustness of the system are further improved through approximation of multiple straight lines and accurate intersection calculation.