299 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 relates to an unmanned aerial vehicle visual guidance precise landing method, and belongs to the technical field of machine vision and unmanned aerial vehicle control. The method comprises the following steps: capturing surrounding environment information through a camera of an unmanned aerial vehicle to obtain operation environment data, and constructing a flight control model through construction parameters of the unmanned aerial vehicle; setting an expected landing point, extracting a target image from the corresponding operation environment data according to the expected landing point, and establishing visual mapping between the unmanned aerial vehicle and the expected landing point based on the target image; the relative position data and the to-be-flied distance of the unmanned aerial vehicle are calculated according to the visual mapping, and a predicted moving trajectory based on the relative position data and the to-be-flied distance is obtained through a flight control model; and an expected landing point is automatically captured and tracked in the landing process, relative pose information between the unmanned aerial vehicle and the expected landing point is solved in real time, and flight parameters of the unmanned aerial vehicle are adjusted in real time according to the relative pose information and the predicted moving trajectory. Visual guidance precise landing of the unmanned aerial vehicle is realized.

The invention relates to an unmanned aerial vehicle cluster cooperative system based on an ad hoc network, which adopts a star topology structure, and the star topology structure takes a leader unmanned aerial vehicle as a center node and performs instruction issuing and data collection with a subordinate unmanned aerial vehicle through direct communication; all the subordinate unmanned aerial vehicles perform task execution around the leader unmanned aerial vehicle, so that centralized management and efficient coordination of tasks are ensured; the leader unmanned aerial vehicle serves as a core node of communication and task command and is responsible for distributing tasks, planning paths and monitoring task progress; under the star topology, part of subordinate unmanned aerial vehicles take other nodes as relays to ensure that the remote unmanned aerial vehicle can receive a task instruction and feed back data to the leader unmanned aerial vehicle, connectivity in a cluster is kept through multi-hop communication, and the remote unmanned aerial vehicle can receive the task instruction. A multi-hop communication mechanism is used to ensure that communication connection and task instruction transmission can still be kept even if a task area is relatively large or the flight distance of the unmanned aerial vehicle is relatively long. And the efficient collaborative operation of the unmanned aerial vehicle cluster in a complex environment is ensured.

A distance measuring device includes: a pixel array unit that performs a light receiving operation for time-of-flight distance measurement using a modulation frequency; a distance measurement processing unit that performs dual mode distance measurement that causes the pixel array unit to alternately perform a light receiving operation using a first frequency as the modulation frequency and a row receiving operation using a second frequency having a higher frequency than the first frequency as the modulation frequency, and single mode distance measurement that causes the pixel array unit to perform only a light receiving operation using a predetermined frequency as the modulation frequency; and a switching processing unit that switches the distance measurement in the dual mode and the distance measurement in the single mode according to a predetermined condition.

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 collaborative optimization task allocation, in particular to an unmanned aerial vehicle cluster collaborative task allocation method and system based on cluster optimization, and the method comprises the following steps: comparing and calculating the matching degree of a task and an unmanned aerial vehicle based on the task emergency degree, the resource demand and the task time limit in combination with the electric quantity, the flight distance and the current position of the unmanned aerial vehicle; analyzing the relevance of the unmanned aerial vehicle and the task through the matching degree, and generating an initial adaptation degree parameter of the task and the unmanned aerial vehicle; according to the method, task execution and path planning are integrated by matching task resource requirements and unmanned aerial vehicle loading capacity, combining resource sharing and time sequence constraint to optimize resource allocation, analyzing and determining task relevance according to task area coupling strength, optimizing resource competition and dynamically adjusting path time consumption and a task sequence; through path reconstruction and state adjustment, resource waste is reduced, task allocation continuity and resource utilization rate are improved, and adaptability and collaboration of an unmanned aerial vehicle cluster in a complex environment are enhanced.

The invention discloses a path planning method and system based on unmanned aerial vehicle surveying and mapping, and relates to the technical field of unmanned aerial vehicle surveying and mapping, and the system comprises a three-dimensional construction unit, a path planning unit and a dynamic obstacle avoidance unit. According to the method, the terrain complexity is scientifically quantified and graded, a quantification basis is provided for subsequent planning, a dividing unit uses a K-means clustering algorithm to accurately divide a surveying and mapping area according to the terrain complexity, the surveying and mapping area is abstracted into a graph structure, a clustering center is used as a node, the weight of an edge is determined in combination with the distance and the terrain complexity, and the surveying and mapping accuracy is improved. The shortest path is searched by means of an A * algorithm, so that the path planning efficiency is effectively improved; the prediction unit optimizes the flight path in the future 30 seconds in a rolling manner with 500 ms as a period, and adjusts the path in real time according to the topographic change, the position of the unmanned aerial vehicle and the obstacle condition, thereby being capable of adapting to terrains with different complexity degrees, reducing the invalid flight distance of the unmanned aerial vehicle, remarkably improving the surveying and mapping efficiency and quality, and reducing the surveying and mapping cost.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly provides a transformer substation unmanned aerial vehicle autonomous inspection route generation method based on an optimization A star algorithm. According to the method, a heuristic function is improved, the Euclidean distance between a target point and a current node is comprehensively considered, obstacle avoidance processing is carried out, and a safer and more efficient inspection route is generated. And by calculating the total cost of each node, the node with the minimum total cost is selected for expansion until a target node is found. According to the improved A star algorithm, the unmanned aerial vehicle can be effectively prevented from flying into an obstacle dense area, the obstacle keeps a safe distance, and the flying safety is remarkably improved. The distance and the obstacle density are comprehensively considered, the generated inspection route is smoother and more efficient, and unnecessary flight distance and energy consumption are reduced.

The invention discloses a real-time three-dimensional multi-machine conflict resolution method based on graph reinforcement learning, and the method comprises the steps: constructing a three-dimensional air traffic interaction scene, carrying out the modeling of the scene into a graph, and obtaining the convolution features of the graph as model input; and establishing a conflict buffer model, and designing a reward function to guide the aircraft to minimize an additional flight distance while ensuring a safety interval. A D3QN reinforcement learning algorithm is adopted for training, the optimal avoidance action of the aircraft is obtained, and real-time multi-aircraft conflict resolution in the high-density airspace is achieved.

The embodiment of the invention provides an unmanned aerial vehicle autonomous obstacle avoidance and exploration method and device, electronic equipment and a medium, and the method comprises the steps: completing the pose estimation of an unmanned aerial vehicle and the construction of a three-dimensional point cloud map based on an RGB image and a depth image through employing a VINS-Fusion algorithm for a dense forest scene, and generating an octree map; a heuristic function and a cost function in the Hybrid A * algorithm are improved, and the improved heuristic function and the improved cost function are obtained; initializing Fast-planner algorithm parameters, based on the octree map, through an improved heuristic function, calculating obstacle cost, updating obstacle three-dimensional coordinates, calculating a flight safety distance, through an improved cost function, traversing local point clouds in a neighborhood range of the unmanned aerial vehicle, searching a path with the minimum cost, extracting an optimal path course, and constructing a safety corridor; and initializing parameters, and completing autonomous obstacle avoidance and exploration in the constructed safe corridor according to the optimal path course.

The utility model discloses a rocket type catching net launching device which comprises gun barrels, gas bombs, a percussion mechanism, a catching net, a cartridge receiver and a folding telescopic rod, the gas bombs are arranged in the corresponding gun barrels, the percussion mechanism is arranged at the tail ends of the gun barrels, the catching net is connected with the gas bombs, and a fixing part is fixedly installed at the front end of an inner cavity of the cartridge receiver. The tail end of the gun barrel is detachably installed on the front side of the fixing component, and the folding telescopic rod is arranged at the front end of the gun barrel. The high-pressure gas cylinder is adopted as launching power, compared with power sources such as ammunition and fuel, higher safety is achieved, materials are easy to obtain, cost is lower, the unfolding angle of the gun barrel can be freely adjusted according to actual requirements, the larger the unfolding angle is, the higher the unfolding speed of the catching net is, and the closer the flying distance is. The method is suitable for close-range combat; otherwise, the smaller the unfolding angle is, the slower the unfolding speed of the catching net is, the farther the flying distance is, and the catching net is suitable for long-distance combat.

The invention discloses a variable gain inversion maneuvering target interception guidance method considering attack angle constraint, and belongs to the technical field of aircraft guidance control. The implementation method comprises the following steps: considering necessary conditions for interception success and end attack angle constraints, based on trajectory shaping, representing a sight angle reference profile by constructing a polynomial function taking a flied distance as an independent variable, and realizing reduction of initial and end constraints; the profile only utilizes missile-target relative information and does not depend on motion information of the target, so that the profile is suitable for intercepting targets in various motion forms. In order to accurately track a line-of-sight reference profile, adverse effects of aircraft control input saturation constraint and target maneuvering interference on a guidance system are considered, an input saturation compensator and a finite time interference estimator are introduced, and a variable gain interception guidance law is designed based on an inversion control architecture. And the aircraft can accurately track the profile and can intercept the maneuvering target at an expected angle at the same time. The method has the advantages of low required overload, high interception precision and good robustness.

The invention relates to an unmanned aerial vehicle cluster cooperative control method for target search in a dynamic obstacle environment. Compared with the prior art, the unmanned aerial vehicle cluster cooperative control method solves the defects that an unmanned aerial vehicle cluster is low in target search efficiency and poor in obstacle avoidance performance in a complex and dynamic environment. The method comprises the following steps: initializing an unmanned aerial vehicle cluster; sensing a dynamic environment and acquiring obstacle information; searching a multi-subgroup collaborative optimization target of the unmanned aerial vehicle; unmanned aerial vehicle cluster path planning and obstacle avoidance processing; and updating the dispatching position of the unmanned aerial vehicle. According to the method, real-time environment perception, a data fusion technology and an obstacle avoidance mechanism based on a force field are combined, so that the safety and stability of the unmanned aerial vehicle cluster in a complex environment are ensured, and possible collision and path planning errors of the unmanned aerial vehicles in a dynamic obstacle environment are avoided; according to the path optimization method based on the cost matrix, the total time and the flight distance of task completion are effectively reduced, and the task execution efficiency is further improved.

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 discloses an inspection waypoint determination method and device, electronic equipment, a storage medium and a product. The inspection waypoint determination method comprises the following steps: acquiring three-dimensional point cloud data of a tower to be detected; determining longitudes, latitudes and heights of a plurality of target points according to the three-dimensional point cloud data; the inspection waypoint is determined according to the latitude and longitude, the height and the unmanned aerial vehicle pod parameters of each target point, the unmanned aerial vehicle pod parameters comprise the pod type, the safe flight distance and the pitching direction measurement range, and the pod type is a visual pod or a partial discharge pod. According to the technical scheme, inspection waypoints can be planned for different inspection tasks according to the pod type of the unmanned aerial vehicle, the flexibility of waypoint planning is improved, and the method can be suitable for various practical application scenes; and the safe flight distance and the pitching direction measurement range of the pod are considered during waypoint planning, so that the inspection efficiency and the resource utilization rate are improved, and the inspection safety is ensured.

The invention discloses an unmanned aerial vehicle flight path optimization method, and belongs to the technical field of unmanned aerial vehicle flight path planning. In order to improve the flight safety performance of the unmanned aerial vehicle, surrounding object information data, environmental factor data and unmanned aerial vehicle information data in the operation process of the unmanned aerial vehicle are collected; dividing the standardized data into three groups of factor data sets; constructing an unmanned aerial vehicle flight minimum safe distance calculation method, and calculating unmanned aerial vehicle minimum safe flight distances at different time points; constructing an unmanned aerial vehicle flight minimum safety distance prediction model, calculating unmanned aerial vehicle flight minimum safety distances under different single environment factors, and calculating unmanned aerial vehicle flight comprehensive minimum safety prediction distance; constructing a collision early warning method based on the comprehensive minimum safety prediction distance of the flight of the unmanned aerial vehicle, and performing early warning on the condition that the distance between the unmanned aerial vehicle and the surrounding object does not exceed the comprehensive minimum safety prediction distance; and carrying out design optimization on the flight path of the unmanned aerial vehicle. The flight path of the unmanned aerial vehicle is dynamically adjusted to keep a safe distance.

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 a cluster cooperative interference planning method based on an optimal interference planning model, and the implementation steps are: constructing a cluster cooperative system including at least two jammers and one mission aircraft, constructing a planning model objective function according to the flight distance of the mission aircraft and the ratio of the power of the jammer to the target radar and the distance between the jammer and the target radar under the cluster system; using a multi-objective particle swarm algorithm to optimize the objective function of the interference planning model, generating an optimal solution set, each group of parameters in the optimal solution set corresponds to its corresponding interference planning scheme, and obtaining multiple interference planning schemes about the spatial position of each jammer, the power allocation of each jammer to the radar, and the flight route of the mission aircraft. The invention improves the interference effect of multiple jammers interfering with radars and the utilization efficiency of interference resources.

The invention belongs to the technical field of digital processing, and discloses an automatic take-off and landing method and system based on an unmanned aerial vehicle field. An environment data set and a hardware data set are collected and preprocessed, the environment data set comprises wind speed data, temperature data, humidity data and extreme weather data, and the hardware data set comprises endurance data, communication data and positioning precision data; processing the environment data set to obtain a flight reference value, and classifying the flight reference value to obtain a flight suggestion result; processing the flight suggestion result to obtain a flight state decision suggestion; and processing the flight state decision suggestion and the hardware data set to obtain a flight distance result, and generally, the method has the remarkable advantages of high capability of assisting in identifying the environment state, high use safety guarantee of the unmanned aerial vehicle and great effect of assisting in identifying the use state of the unmanned aerial vehicle.

The invention provides a complex area unmanned aerial vehicle surveying and mapping mode decision and path planning method, and relates to the technical field of unmanned aerial vehicles. According to the method, aiming at the defect that traditional unmanned aerial vehicle surveying and mapping cannot automatically adapt to different terrains in a terrain with large height fluctuation, flight modes of an unmanned aerial vehicle at different terrain heights are decided based on a height fall threshold algorithm, and surveying and mapping sub-regions adopting different flight modes are generated; the complexity of manual flight mode setting and surveying and mapping area delimiting is avoided, and it is ensured that each surveying and mapping sub-area can adopt the optimal surveying and mapping path according to the topographic characteristics of the surveying and mapping sub-area. Performing path planning on each surveying and mapping sub-region, and recording coordinates of a driving-in / driving-out point of each surveying and mapping sub-region; a mathematical model of coverage path planning of multiple surveying and mapping sub-regions of the single unmanned aerial vehicle is constructed, and a path which passes through all task points in the to-be-surveyed and mapped region and has the shortest flight distance is obtained based on a variable neighborhood search VNS-genetic algorithm, so that the surveying and mapping efficiency and precision are improved.

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 data processing, and discloses a bridge construction violation behavior monitoring method, device and equipment and a storage medium, and the method comprises the steps: determining an effective monitoring time period of a construction monitoring point through obtaining a planned time period of executing each construction process in each bridge construction section; according to the monitoring type information and the monitoring risk level of each construction monitoring point, taking an image acquisition distance interval corresponding to the monitoring type information and an image acquisition frequency interval corresponding to the monitoring risk level as constraints, taking a flight distance as an optimization target, and optimizing and solving an unmanned aerial vehicle image acquisition path planning strategy; according to the method, the unmanned aerial vehicle is controlled to execute flight, image acquisition and bridge construction violation judgment, and an unmanned aerial vehicle image acquisition scheme meeting different monitoring distances and different monitoring frequencies of different construction procedures is provided, so that the bridge construction violation behavior monitoring accuracy and efficiency are improved; the monitoring duration of the illegal behavior monitoring unmanned aerial vehicle is prolonged, and the method has good scene adaptability.

The invention discloses a WRSN charging method and system based on the lowest energy consumption of an unmanned aerial vehicle, and the method comprises the following steps: S1, taking the minimization of the total energy consumption of the unmanned aerial vehicle as a target, constructing a flight-hovering-based charging problem, and decomposing the flight-hovering-based charging problem into a minimization flight energy consumption problem and a minimization charging energy consumption problem; s2, aiming at the problem of minimizing charging energy consumption, providing an adaptive charging strategy based on flight and hovering charging modes, and carrying out joint optimization on the charging mode, the flight speed and the charging coverage radius when the unmanned aerial vehicle executes a charging task; s3, for a minimum flight energy consumption problem, decomposing the problem into an optimal non-charging flight speed determination problem and a minimum flight distance problem through a function decomposition mode; the optimal non-charging flight speed is obtained through a tangent method, and the problem of minimum flight distance is solved through a genetic algorithm. According to the invention, the total energy consumption of the unmanned aerial vehicle during the charging task can be effectively reduced, and the time complexity is relatively low.

The invention provides an unmanned aerial vehicle cooperative area coverage flight path optimization method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle flight path optimization, and the method comprises the steps: analyzing and obtaining flight path control parameters of each unmanned aerial vehicle in a flight path segment in an unmanned aerial vehicle formation, the flight path control parameters comprise a flight distance, a yaw angle variation and a maximum relative position distance between the two unmanned aerial vehicles; acquiring the interception probability of the passive radar system on the radio frequency signal of the unmanned aerial vehicle; establishing a radio frequency stealth-oriented track optimization model based on the track control parameters and the interception probability; improving a particle swarm optimization algorithm; and solving the track optimization model based on an improved particle swarm optimization algorithm. The radio frequency stealth performance of the unmanned aerial vehicle formation is effectively improved.