175 results about "Flight distance" patented technology

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention relates to the technical field of communication data processing, and provides an unmanned aerial vehicle data transmission link construction method for ecological monitoring, and the method comprises the steps: obtaining an ecological monitoring unmanned aerial vehicle operation parameter sequence, and calculating disturbance feature coefficients of ecological monitoring message data of a ground user receiving end at different moments; obtaining an unmanned aerial vehicle distribution space, calculating an air-ground space distance between the ecological monitoring communication unmanned aerial vehicle and a ground user receiving end, calculating a flight space distance between the ecological monitoring communication unmanned aerial vehicle and the ecological monitoring communication interference unmanned aerial vehicle, and calculating dynamic communication quality parameters of different ecological monitoring unmanned aerial vehicles at different moments; and calculating fading loss values of different ecological monitoring unmanned aerial vehicles at different moments, and selecting an ecological monitoring communication unmanned aerial vehicle data transmission link according to the fading loss values of the ecological monitoring unmanned aerial vehicles. The stability of the communication state of the transmission link of the ecological monitoring unmanned aerial vehicle is ensured.

The invention provides an unmanned aerial vehicle distribution task scheduling optimization method considering an endurance state, and belongs to the field of intelligent manufacturing. The method comprises the following steps: aiming at the problems of takeoff sequence, task allocation and endurance state change of an unmanned aerial vehicle in a distribution task, constructing a power consumption model based on flight stage division; under constraint conditions of unmanned aerial vehicle electric quantity, loading capacity, safe flight height and the like, converting the scheduling scheme into a three-layer integer sequence including a take-off sequence, a task allocation sequence and an execution sequence for expression and optimization; a task interruption and battery replacement decision-making mechanism and a battery replacement station selection cost function under the condition of insufficient electric quantity are introduced, and a swarm intelligence algorithm is adopted for solving, so that the optimization target of minimizing the total task completion time, the total battery replacement frequency and the flight distance standard deviation is achieved.

The invention relates to the technical field of bird recognition, in particular to an infrared target image multi-target recognition method based on image analysis. According to the flight vector of the bird area in each frame of the environment infrared image, carrying out optical flow tracking on pixel points of the bird area in each frame of the environment infrared image, and according to the pixel point position distribution of the bird area in each frame of the environment infrared image and the consistent condition of the bird area and the movement direction of the pixel points, adjusting the movement condition of the pixel points in the bird area. And acquiring the flight distance of the bird area in each frame of the environment infrared image, and determining the risk degree of the bird area in each frame of the environment infrared image by combining the distance between the bird area and the facility area in the direction of the flight vector. According to the invention, the optical flow change caused by the wing movement is accurately adjusted in different flight states, so that the overall flight analysis of the bird is more accurate, and the accuracy of the judgment of the danger degree of the bird is improved.

The invention provides an unmanned aerial vehicle cluster surveying and mapping task dynamic allocation and path optimization method, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the following steps: S1, carrying out the multi-source data fusion and dynamic task modeling, generating a single unmanned aerial vehicle initial path, and carrying out the multi-unmanned aerial vehicle cooperative adjustment; s2, task allocation based on an improved ant colony algorithm; s3, collaborative path optimization and conflict avoidance; s4, task dynamic redistribution and cluster collaboration; the dynamic adaptability is high, dynamic adjustment of task allocation is achieved through real-time fusion of multi-source data, the response time is shorter than 2 s, and the emergency scene task completion rate is increased by 30%; global optimization is remarkable, the improved ant colony algorithm is combined with collaborative path planning, the total flight distance of the cluster is reduced by 25%, and the surveying and mapping efficiency is improved by 40%; robustness is prominent, a dynamic redistribution mechanism effectively processes unmanned aerial vehicle faults, environment sudden changes and other abnormalities, and the task redistribution success rate is larger than 95%. The precision is guaranteed, and the terrain complexity is matched with the load type, so that the percent of pass of surveying and mapping data is improved from 82% to 96%.

The invention provides a multi-unmanned aerial vehicle task allocation and path planning dual-stage joint optimization method and system, and belongs to the technical field of unmanned aerial vehicle path planning. According to the invention, by constructing a novel three-dimensional multi-unmanned aerial vehicle task allocation and path planning dual-stage joint optimization framework, collaborative optimization of task allocation and path planning is realized; wherein the RL-TPTA algorithm in the task allocation stage dynamically optimizes a task access sequence by means of K-means clustering and reinforcement learning, the total flight distance of the unmanned aerial vehicle can be effectively reduced, and the adaptability in a large-scale task scene is higher; the MSM-EABC algorithm in the path planning stage is higher in convergence speed through multi-strategy fusion, and can efficiently jump out of local optimum and avoid three-dimensional environment collision risks; the two-stage framework combines the advantages of the two, the path length can be remarkably shortened in different-scale task scenes, the convergence efficiency is improved, the operation time is shortened, the adaptability to the complex three-dimensional environment is better, and the task allocation and path planning coupling optimization problem in the three-dimensional unmanned aerial vehicle cluster search and rescue after disaster is solved.

The invention discloses an autonomous inspection task allocation optimization method for a low-altitude flight unmanned aerial vehicle, and belongs to the technical field of intelligent control of unmanned aerial vehicles. The method comprises the following steps: acquiring task attributes, an unmanned aerial vehicle state, a geographical environment and real-time meteorological data through a data acquisition module; the environment modeling module is fused with geographic and meteorological data to construct a three-dimensional dynamic environment model; the dynamic allocation module takes the minimization of the number of times of operation of the unmanned aerial vehicle as a target, combines the single maximum flight distance of the unmanned aerial vehicle and path safety threshold constraints, adopts an improved saving algorithm to cluster to perform task grouping, optimizes a task sequence based on a vehicle path problem model with a time window, and generates a task allocation scheme; and the control terminal issues a task allocation scheme to the unmanned aerial vehicle, the unmanned aerial vehicle executes the scheme and adjusts a path according to real-time meteorological changes, and a multi-dimensional inspection report is generated after the task is completed. According to the invention, the task allocation efficiency and the flight safety in a complex environment are improved, and the inspection cost is reduced.

This invention relates to the field of communication data processing technology and proposes a method for establishing a data transmission link for an ecological monitoring drone. The method comprises: obtaining a sequence of operating parameters for an ecological monitoring drone and calculating the disturbance characteristic coefficients of ecological monitoring message data from a ground user receiving end at different times; obtaining the drone distribution space, calculating the air-ground distance between the ecological monitoring communication drone and the ground user receiving end, calculating the flight distance between the ecological monitoring communication drone and an ecological monitoring communication interfering drone, and calculating the dynamic communication quality parameters of different ecological monitoring drones at different times; calculating the fading loss values ​​of different ecological monitoring drones at different times, and selecting an ecological monitoring communication drone data transmission link based on the fading loss values ​​of the ecological monitoring drones. This method ensures the stability of the communication state of the ecological monitoring drone transmission link.

The invention discloses an air-ground task intersection prediction method for emergency rescue, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of accurately predicting an air-ground task intersection with reachability and energy efficiency requirements, the method comprises the following steps: predicting an unmanned aerial vehicle path; predicting a ground vehicle path; generating candidate intersection points; calculating the time cost of the rescue task, the energy consumption cost of flight and the environmental risk cost; constructing a comprehensive cost function; constructing a confidence factor through the terrain height difference, the path node complexity and the communication shielding score, and carrying out risk correction on the comprehensive cost function to obtain a corrected comprehensive cost function; and introducing factors such as a ground slope angle, a wind speed value, a ground trafficability score and an unmanned aerial vehicle flight distance to carry out punishment correction, and extracting an optimal air-ground task intersection point coordinate for emergency rescue. The method is suitable for various emergency rescue, multi-platform cooperation and intelligent task scheduling, and has wide application prospect and popularization value.

The invention discloses a transformer substation flood prevention intelligent early warning method and system, and relates to the technical field of flood prevention intelligent early warning, and the method comprises the following steps: obtaining transformer substation ponding historical data and an unmanned plane observation image, building a ponding risk state set based on the ponding historical data, and carrying out the correlation analysis, performing feature fusion on the ponding state vector and an unmanned aerial vehicle observation image to obtain a first ponding state sequence, and mapping the first ponding state sequence to an unmanned aerial vehicle observable area to obtain a dynamic target area set; according to the dynamic target area set and a preset unmanned aerial vehicle safety distance constraint, dynamically adjusting an unmanned aerial vehicle inspection path to obtain a first inspection path; according to the method, the unmanned aerial vehicle is subjected to routing inspection along the first routing inspection path, intelligent substation flood prevention early warning is performed, and the problem that local key ponding points are difficult to observe in real time in a traditional flight mode due to the fact that the unmanned aerial vehicle needs to keep a safe flight distance in the routing inspection process and cannot be directly close to high-voltage equipment or other key facilities is solved.

The invention relates to the technical field of unmanned aerial vehicle path planning in a complex environment, and provides a complex environment unmanned aerial vehicle path planning method based on Chebyshev chaotic mapping sparrow optimization. Under the constraint of flight distance and height, a total cost function is constructed, Chebyshev chaotic mapping is utilized to initialize a sparrow population, population diversity is improved, global search and local development capabilities in an exploration rate balance position updating process are introduced, a producer and follower step length adjustment strategy based on the exploration rate is adopted, and a sparrow swarm optimization algorithm is established. According to the method, the self-adaptability of the step length in the position updating process of the producer and the follower is improved, the minimization of the total cost function is realized in the process from the early global search of the minimization of the total cost function to the later focusing local search, the optimal solution of the total cost function is sought, and the flight path which is short in distance, low in energy consumption and capable of avoiding the threat source is planned for the unmanned aerial vehicle.

The invention discloses a multi-unmanned aerial vehicle electric power inspection task allocation method and system, and the method comprises the steps: building an optimization objective function according to the constraints of a total flight distance, an average flight height, and a terrain threat; by improving a random step size search strategy based on distance weight, the predation stage of the black wing plinualism algorithm is optimized, and the global search ability of the algorithm is enhanced; an optimal individual selection strategy based on fitness and distance is introduced, and the global search efficiency and optimization precision of the black-wing plinary algorithm in the migration stage are enhanced; a cooperative predation mechanism of the Arctic Sea-Sharrow algorithm is introduced, positions are updated through individual cooperation, and the ability of the algorithm to jump out of local optimum is effectively improved. The method has remarkable advantages in the aspects of task allocation efficiency and precision in complex mountain environment simulation, collision-free paths between generated task points are shorter, and an efficient solution is provided for unmanned aerial vehicle task allocation and path planning under complex terrains.

The invention relates to a multi-unmanned aerial vehicle cooperative path planning and scheduling method and system. The method comprises the following steps: acquiring task requirements, and determining a plurality of task areas needing aerial survey, coverage requirements and unmanned aerial vehicle parking positions; constructing a coverage path with the minimum turning cost for each task area according to coverage requirements; calculating the flight path cost of the unmanned aerial vehicle according to the parking position and the coverage path of the unmanned aerial vehicle; and according to the flight path cost, planning an access area and an access sequence for each unmanned aerial vehicle by using a genetic algorithm, and generating an unmanned aerial vehicle flight path. According to the method, the task demand is acquired, the unmanned aerial vehicle observation coverage path with turning optimization is generated in the task area, and the optimized unmanned aerial vehicle scheduling scheme and flight path are generated by using the genetic algorithm based on the coverage path cost, so that the surveying and mapping demand can be met while the quality and quantity are guaranteed; the total flight distance is reduced to the maximum extent, the workload distribution among the unmanned aerial vehicles is balanced, and a data acquisition window for large-scale surveying and mapping is shortened.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle forced landing control system and method.The unmanned aerial vehicle forced landing control system comprises an unmanned aerial vehicle state recognition module, an unmanned aerial vehicle forced landing estimation module, a forced landing processing module and a forced landing evaluation module, and the unmanned aerial vehicle forced landing estimation module comprises a landing point position recognition unit, a landing risk evaluation unit and an early warning execution unit; the forced landing evaluation module comprises a landing self-checking unit and a dispatching recovery unit. The system analyzes the current forced landing state through an unmanned aerial vehicle state recognition module, evaluates the flight distance of the unmanned aerial vehicle in real time, obtains a preliminary landing point after preliminarily screening landing points, judges whether the risk coefficient of the preliminary landing point exceeds a preset warning value after screening out the preliminary landing point, and gives an alarm if the risk coefficient exceeds the preset warning value. The system can give risk early warning in time and implement corresponding strategies, and measures are taken quickly to prevent the unmanned aerial vehicle from landing at a high-risk point location, so that the safety and reliability are enhanced.

A shot identifying module 220 identifies a flight distance of the golf ball and an angle of a direction from the measurement terminal 10 toward the golf ball with respect to a direction toward which the user terminal 10 is directed at a time of the measurement. A terminal position and orientation identifying module 222 identifies a terminal position and a terminal orientation. A ball position identifying module 224 identifies a ball position based on the terminal position, the terminal orientation, the flight distance of the golf ball, and the angle.

The utility model discloses a golf tee which comprises an inserting rod, a ball supporting part is arranged at the top end of the inserting rod, a plurality of supporting corners are arranged on the periphery of the ball supporting part and used for supporting a golf ball, and the inserting rod is arranged in a flat shape. Due to the flat design of the inserting rod, the contact area of the golf club and the golf tee is reduced, so that the friction force is reduced, the ball hitting is smoother, and the flying distance of the ball is increased. The design is simple in structure and convenient to operate.

The invention provides an unmanned aerial vehicle task allocation method based on an improved South goose optimization algorithm, and the method comprises the steps: generating a plurality of initial allocation schemes in a three-dimensional environment according to the starting points and target points of a plurality of unmanned aerial vehicles; calculating the distance from the starting point to the target point of all unmanned aerial vehicles, and establishing a task allocation model fusing the longest single-aerial-vehicle flight distance and the shortest total flight distance; calculating the comprehensive flight cost of all the initial schemes; and performing iterative optimization on the initial allocation scheme by using an improved South goose optimization algorithm to obtain an optimal allocation scheme, so that the longest single-machine flight distance and the total flight distance are both effectively reduced. In the iterative optimization process, a new region search mechanism is designed, a mixed strategy with development as a main part and exploration as an auxiliary part is formed, an adjustment coefficient is added in the development stage, and the search range is further expanded. According to the method, the real-time performance and the task completion efficiency of multi-unmanned aerial vehicle task allocation are remarkably improved, and the method is suitable for scenes with strict task allocation requirements such as disaster rescue and logistics distribution.

A time-of-flight distance measuring method and TOF distance measuring system. The distance measuring method includes: intermittently transmitting a plurality of pulses from a pulse generation unit; controlling a TOF sensor to allow each pixel of a plurality of pixels in the TOF sensor to continuously perform a first signal sampling first proportion for a first predetermined time on a first proportion of a plurality of reflected signals and performs a second signal sampling second proportion for a second predetermined time on a second proportion of a plurality of reflected signals, so as to generate a plurality of sampling results corresponding to the plurality of pixels; obtaining a plurality of depth information and a plurality of luminance information corresponding to the plurality of pixels according to the plurality of sampling results; and adjusting the first proportion and the second proportion according to the plurality of depth information and the plurality of luminance information.

An object of the present disclosure is to provide a golf ball having improved flight distance on driver shots and excellent spin performance on approach shots (particularly under the condition that there is grass between the golf ball and the club face) and on middle iron shots. The present disclosure provides a golf ball comprising a spherical core and a cover disposed outside the spherical core, wherein when a center hardness of the spherical core (Shore C hardness), hardnesses at 2.5 mm, 5 mm, 7.5 mm, 10 mm, 12.5 mm and 15 mm points from a center of the spherical core toward a surface of the spherical core (Shore C hardness), and a surface hardness of the spherical core (Shore C hardness) are represented by H0, H2.5, H5, H7.5, H10, H12.5, H15 and Hs respectively, the following relation is satisfied:(H2.5−H10)>(H12.5−H10)>(Hs−H15).

The present application relates to the technical field of low-altitude aircraft, in particular to a pre-turning flight ball car control method and a pre-turning flight ball car, wherein the pre-turning flight ball car control method comprises the following steps: S1, obtaining a pre-turning direction and a current direction; S2, calculating a pre-turning angle and a pre-turning adjustment direction based on the pre-turning direction and the current direction; S3, controlling the pre-turning device to drive the flight driving device or the combination of the flight driving device and the cabin to turn relative to the landing gear to face the pre-turning direction according to the pre-turning angle and the pre-turning direction. In this way, the problems of the existing golf cart, such as limitation, and the traditional aircraft, such as increased flight distance and poor passenger experience caused by adjusting the flight direction after take-off, are solved.

The application discloses a kind of unmanned aerial vehicle autonomous inspection orthographic image generation method, it is related to unmanned aerial vehicle surveying and mapping and autonomous navigation technical field, the path optimization strategy of the present application is fused by genetic algorithm, global optimization is realized to route in combination with the image parameter constraint of inspection area grid feature, with candidate navigation point as node, through the smoothness factor of navigation section, redundant navigation point is filtered, and the complexity of route is reduced, genetic algorithm is constrained with flight height, navigation point spacing, constructs fitness function including flight distance, number of turns, overlap rate, finds global optimal path by population iteration, optimizes and balances multi-objective demand, ensures that route meets image acquisition accuracy requirement, can also shorten flight distance, reduce number of turns, adapt to the endurance of unmanned aerial vehicle, guarantee that inspection task is efficiently promoted, simultaneously, the navigation point coordinates of simulation output are directly adapted to flight control system, ensure that actual flight parameter is consistent with planning parameter.

The invention discloses an intelligent attendance checking method, device and equipment based on multi-dimensional characteristics and a medium, and the method comprises the following steps: collecting original signal data of network equipment in an attendance checking area, and carrying out characteristic extraction on the original signal data to respectively obtain a received signal strength characteristic, a channel state characteristic and a flight distance characteristic; performing feature fusion on the received signal strength feature, the channel state feature and the flight distance feature to obtain a multi-source feature vector, and inputting the multi-source feature vector into a preset random forest model to enable the random forest model to output an event judgment result of the target user terminal; determining an attendance checking result of the user according to the event judgment result, pre-acquired event occurrence time and an attendance mode of the corresponding user; wherein the attendance mode is determined through a preset Gaussian mixture model. The problem that attendance checking judgment is inaccurate due to the fact that attendance checking rules are rigid in the prior art can be solved.

The application provides a flapping-wing aircraft formation flight energy-saving method and device, the method comprising: inputting geometric parameters of a required control flapping-wing aircraft; calculating a required relative flight distance to be kept when the flapping-wing aircraft flies in formation according to the input geometric parameters of the flapping-wing aircraft; determining a lead aircraft and wingman according to an evaluation function selection to form a formation flight; detecting a residual power of a current lead aircraft, and replacing the lead aircraft when the current lead aircraft consumes energy exceeding a set value; and continuously replacing the lead aircraft before the end of a flight task, and selecting a new lead aircraft according to the evaluation function. The method can keep the energy distribution of the flapping-wing aircraft group balanced when the flapping-wing aircraft flies in formation, effectively saves the energy of the flapping-wing aircraft, and improves the endurance of the flapping-wing aircraft.

The application relates to a UAV attitude stabilizing control system in a mountainous high-altitude environment. The system comprises a screening module, which obtains a matching coefficient according to the distance required for each candidate route to converge into a preset route, wind power information and wind direction information on the candidate route, and residual power and residual flight distance, and screens a target route from multiple candidate routes; a calculation module, which calculates reverse compensation forces corresponding to multiple force directions according to the force intensity of the UAV in the multiple force directions in a first historical period; a determination module, which determines the target force of each motor of the UAV according to the wind power at the current position, the distance between the current position and each obstacle, the frequency of the UAV producing deviation in a second historical period, and the reverse compensation forces; and a control module, which determines the target speed of the UAV according to the angle between the total force vector of the UAV at each position of the target route and the target route. The application can make the UAV control more stable when flying in a mountainous high-altitude environment.

The invention discloses an intelligent route planning method and system for an unmanned aerial vehicle. The method comprises the following steps: acquiring mark point data and real-time hotspot data; scoring each mark point according to the mark point data and the real-time hotspot data, and determining the priority of the mark points by sorting the scoring results; constructing a multi-objective optimization function which meets a preset constraint condition and takes flight distance minimization, hotspot coverage rate maximization, priority satisfaction degree maximization and risk minimization as objectives; wherein the constraint conditions comprise a battery endurance constraint, an airspace limitation constraint, a weather condition constraint and a time window constraint; and scoring each randomly generated route through the multi-target optimization function, and determining the route with the minimum score as a target optimal route. According to the method, the response capability to a real-time dynamic environment can be effectively improved, multiple targets such as distance, coverage, priority and risk are balanced, and dynamic, efficient and safe unmanned aerial vehicle optimal route planning is realized.

The application belongs to the technical field of unmanned aerial vehicle information security, and particularly relates to an anti-deception interference data protection method and system for power inspection unmanned aerial vehicle, which first acquires the flight speed and position information of the unmanned aerial vehicle in real time, and dynamically adjusts the actual flight constraint radius of the unmanned aerial vehicle according to the comparison result of the flight speed and a preset threshold value. The actual flight distance between the unmanned aerial vehicle and the nearest route point is calculated, and compared with the actual flight constraint radius to determine whether the unmanned aerial vehicle is subjected to position deception interference. In the case that the position of the unmanned aerial vehicle is safe, a token transmission mechanism is used to receive the inspection data transmitted by the unmanned aerial vehicle, and the time window of data transmission is dynamically adjusted according to the flight speed. Through real-time acquisition of the flight speed and position information of the unmanned aerial vehicle, and dynamic adjustment of the flight constraint radius and the data transmission strategy according to the flight speed and position information, dynamic identification and response to deception interference are realized.

The invention relates to a device for attaching an additional incapacitating element in the form of a net to a munition and is used for incapacitating airborne targets, preferably targets with propellers. The device for attaching an additional incapacitating element to a munition is an annular attachment designed to be seatable on a munition, the attachment having flexible elements that form a net and being housed in a container that has a through-opening and double walls, one wall being an outer wall and the other being an inner wall, with the net disposed therebetween, wherein the inside diameter of the annular attachment is greater than the diameter of the head of the munition but less than the maximum diameter of the munition. The technical result is that of simplifying the structure of the device for attaching an additional incapacitating element in the form of a net to a munition, while also increasing the flight range of a net as a means for incapacitating airborne targets.

The invention provides a brake device and a fishing reel, which not only can achieve miniaturization and low cost, but also can supply optimal braking force in a wider speed range, and can more practically realize improvement of flight distance and prevention of recoil. Specifically, the fishing reel according to one embodiment of the present invention is provided with: a spool on which a fishing line can be wound; a reel body that rotatably supports the spool; a winding unit capable of operating the spool in a winding direction; a switching unit capable of switching whether or not the reel can be unwound; the electromagnetic braking part is used for carrying out electromagnetic braking on the winding drum; a friction brake unit for imparting a frictional force to the spool; and an adjustment member that adjusts a braking force on the spool, the adjustment member being configured so as to be able to adjust both the electromagnetic brake unit and the friction brake unit.

The invention relates to an unmanned aerial vehicle appearance inspection method and system based on a large model and intelligent glasses, and relates to the field of unmanned aerial vehicle detection, and the method comprises the steps: obtaining the model and the historical abnormal position of an unmanned aerial vehicle and the task information of a to-be-executed task, and determining the inspection process of the unmanned aerial vehicle according to the model and the historical abnormal position, the task information comprises a flight distance, a flight duration and a wind speed, controlling the intelligent glasses to output an inspection process, so that a person acts according to the inspection process and collects a plurality of appearance images of the unmanned aerial vehicle, and inputting the plurality of appearance images into a plurality of trained anomaly detection models for anomaly detection. And obtaining a detection result of each anomaly detection model, and judging whether the unmanned aerial vehicle is allowed to release or not according to the multiple detection results and the task information. The appearance state of the unmanned aerial vehicle can be quickly determined when the unmanned aerial vehicle needs to execute the emergency task, and whether release is allowed or not is accurately judged.