766 results about "Drone flies" patented technology

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 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.

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 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 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 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 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 provides an unmanned aerial vehicle-based high-rise hyperbolic glass curtain wall automatic inspection method and system, and the system comprises an unmanned aerial vehicle, a data acquisition module and a controller. The data acquisition module is carried on the unmanned aerial vehicle and comprises an ultrasonic sensor array, an infrared thermal imager and an image acquisition module; the controller is used for performing time-frequency domain analysis on the reflected wave to obtain the signal intensity of the reflected wave; the controller performs temperature gradient calculation on the structural adhesive surface temperature distribution image to obtain temperature anomaly region features, obtains optical image textures according to the surface optical image, and outputs an inspection result according to the reflected wave signal intensity, the temperature anomaly region features and the optical image textures; generating an unmanned aerial vehicle flight path according to the three-dimensional model and the curvature parameter of the hyperbolic glass curtain wall, generating obstacle avoidance position information according to environment information set by the hyperbolic glass curtain wall, and updating the unmanned aerial vehicle flight path according to the obstacle avoidance position information; and controlling the unmanned aerial vehicle to automatically inspect the hyperbolic glass curtain wall according to the flight path of the unmanned aerial vehicle.

The invention relates to the field of deep learning, and provides an unmanned aerial vehicle flight data anomaly detection method and system, and electronic equipment, and the method comprises the steps: obtaining unmanned aerial vehicle simulation flight data as a source domain, and obtaining real unmanned aerial vehicle flight data as a target domain; selecting a plurality of simulated flight data with the highest similarity for model training and integrated migration; training a pre-constructed neural network basic prediction model, and performing fine tuning and evaluation by using the target domain; obtaining a prediction output result of a final target domain and a final prediction residual sequence for an anomaly judgment basis; and performing anomaly detection on the flight data of the to-be-detected unmanned aerial vehicle by adopting the dynamic detection threshold. The method and the device are used for solving the defects of low precision and insufficient robustness of unmanned aerial vehicle flight data anomaly detection under limited samples in the prior art, knowledge migration is performed by fully utilizing data information of multiple source domains, excessive dependence on a single source domain feature is avoided, and thus the model precision is improved.

The invention provides a low-altitude heterogeneous unmanned aerial vehicle flight plan pre-tactical diversified deployment method and system, and belongs to the technical field of air traffic management and flight supervision information, and the method comprises the steps: generating an initial flight plan of a single machine, and deducing a process flight path of the initial flight plan; performing multi-aircraft track conflict detection, and identifying static and dynamic flight conflicts; heterogeneous unmanned aerial vehicle multi-factor priority evaluation is carried out, and the space resource use sequence of unmanned aerial vehicles is determined; diversified strategies such as flight routes, take-off time and flight speed are designed, and optimal release is achieved. According to the method, the optimal conflict resolution of the heterogeneous unmanned aerial vehicles in the low-altitude airspace in the pre-tactical stage is realized, the conflict of multiple unmanned aerial vehicles on limited airspace time and space use is effectively avoided, and the requirements of reasonable use and continuous safety of low-altitude airspace resources are met.

The invention relates to an unmanned aerial vehicle illumination control method and system based on three-dimensional model data. The method comprises the steps of obtaining three-dimensional model data, environment perception data and flight positioning data, and analyzing a target scene and an unmanned aerial vehicle illumination demand. And based on the data, planning the flight path of the unmanned aerial vehicle, and generating flight path planning data. And in combination with environmental perception information, performing illumination analysis on the main illumination end, calculating main illumination power density distribution and a main illumination angle, and identifying a main illumination blind area. And for the blind area, light supplement analysis is performed by using environmental perception data, and blind area illumination power density distribution and a blind area illumination angle are calculated. And finally, synthesizing the main illumination data and the blind area light supplementing data to generate illumination control information of the unmanned aerial vehicle. By adopting the method, the illumination strategy can be intelligently adjusted under different environmental conditions, the illumination effect, the illumination precision and the illumination uniformity of the unmanned aerial vehicle can be effectively improved, the energy utilization efficiency is also improved, and unnecessary energy consumption is reduced.

The invention is suitable for the technical field of path planning, and particularly relates to an agricultural unmanned aerial vehicle flight path planning method based on an improved Harris eagle algorithm, and the method comprises the steps: constructing a site environment model, and determining a target function based on the site environment model; initializing parameters; using Chebyshev mapping to initialize a population, initializing individuals, and reserving Hallis eagle individuals with higher fitness to form a final initial population; executing a global exploration strategy or a local development strategy; and continuously updating the current globally optimal solution by comparing the fitness values of all individuals in the whole search space, and outputting the fitness value and position of the optimal individual when a stop condition is met. According to the method, Chebyshev mapping, a non-linear change escape energy factor mechanism and a reverse learning crossover operator disturbance strategy are combined, and a safe, reliable and low-cost unmanned aerial vehicle flight path planning scheme is obtained by constructing a flight path planning model fusing constraint conditions such as an unmanned aerial vehicle flight area and a maximum climbing angle.

Provided are a drone and a method of neutralizing an illegal drone performed by the drone. The method includes, by interoperating with a ground management device installed in a protection zone, detecting an illegal drone flying in the protection zone without permission, tracking the illegal drone having mobility using a detection sensor mounted on the drone at the same time as the illegal drone is detected, and by performing a direct collision on a center frame of the tracked illegal drone, capturing the illegal drone on which the direct collision is performed.

The invention relates to the field of underground pipe gallery space detection, in particular to an underground pipe gallery detection method based on unmanned aerial vehicle point cloud scanning, and the method comprises the steps: 1, arranging a reflector plate, and collecting the three-dimensional coordinates of the reflector plate in an absolute coordinate system; 2, performing three-dimensional laser scanning when the unmanned aerial vehicle flies, performing point cloud acquisition at the entrance of the underground pipe gallery, acquiring spatial point cloud data by a laser radar carried by the unmanned aerial vehicle, and recording environment image information by a visual sensor; 3, establishing a relative coordinate system associated with spatial features, and carrying out point cloud SLAM calculation; step 4, calculating overground and underground continuous spatial point clouds based on an SLAM algorithm, and extracting three-dimensional coordinates in the scanning point clouds; 5, converting the point cloud in the relative coordinate system to an absolute coordinate system by using a Rodrigue matrix conversion method; and step 6, acquiring real spatial position information of the underground pipe gallery. The real spatial position of the underground pipe gallery in an absolute coordinate system is obtained through continuous scanning of the unmanned aerial vehicle in combination with ground GNSS measurement.

The invention belongs to the technical field of unmanned aerial vehicle path adjustment, and particularly relates to an unmanned aerial vehicle flight path adjustment method based on obstacle avoidance in a non-visual state, and the method comprises the steps: generating a field three-dimensional basic model; acquiring three-dimensional space data in a task area of the unmanned aerial vehicle and point cloud data in front of a flight path; acquiring a flight path feature sequence table; respectively obtaining an optimal reference path vector and an instant avoidance path vector; outputting a flight path vector for guiding the unmanned aerial vehicle to execute path adjustment by using the path decision model; simulating a non-visual flight scene on the on-site three-dimensional basic model, and analyzing the flight path vector into a flight control instruction for driving the unmanned aerial vehicle to execute; calculating a path adjustment amplitude based on the optimal reference path vector and the flight path vector; and updating the field three-dimensional basic model, and displaying the flight path of the unmanned aerial vehicle in a three-dimensional form. According to the invention, the flight safety and path adaptive ability of the unmanned aerial vehicle in a complex non-visual environment are significantly improved.

The invention discloses an unmanned aerial vehicle crossing risk avoidance training system based on AI behavior prediction, and relates to the technical field of unmanned aerial vehicles, and the system comprises an unmanned aerial vehicle behavior prediction module which predicts obstacles, weather changes and other sudden risks possibly encountered by an unmanned aerial vehicle in the flight process based on the flight path, environment information and sensor data of the unmanned aerial vehicle; the risk evasion strategy generation module is used for generating a real-time evasion strategy according to the predicted risk information; the training data acquisition module is used for collecting flight data of the unmanned aerial vehicle in different flight environments as training samples; and the risk avoidance training module is used for training the unmanned aerial vehicle through a reinforcement learning model based on the collected flight data and the generated risk avoidance strategy. According to the method, obstacles, weather changes and sudden risks in flight are monitored in real time through AI behavior prediction, intelligent evaluation and sorting are performed in combination with multiple risk factors, coping strategies are optimized, and it is guaranteed that the unmanned aerial vehicle safely and efficiently executes tasks under various conditions.

The invention discloses a method, a system, a medium and equipment for identifying and early warning personnel below a low-altitude unmanned aerial vehicle. The method comprises the following steps: S1, data acquisition: acquiring multi-modal data of a lower area; s2, establishing image recognition: establishing a mapping relation between an image coordinate system and a ground projection area according to the real-time height of the unmanned aerial vehicle and IMU attitude data; s3, outputting an identification result: performing target identification on the dynamic region of interest by adopting a visual detection model and a thermal imaging segmentation model which are connected in parallel; s4, feature extraction: performing time sequence attitude feature extraction on the identified personnel target, classifying personnel motion states by using an LSTM network, and predicting a target motion track in combination with Kalman filtering; and S5, completing early warning: constructing a dynamic risk assessment matrix according to the flight height, the personnel density and the movement speed of the unmanned aerial vehicle, and triggering a graded early warning signal when a risk value exceeds an adaptive threshold value. The method has the advantage that the control safety of the unmanned aerial vehicle is improved.

The invention relates to the technical field of environment perception and three-dimensional modeling, and discloses an unmanned aerial vehicle flight security early warning method and system for a complex environment, and the method comprises the steps: collecting the environment data of a complex flight scene, and enabling the environment data to comprise a dynamic obstacle, a static topographic feature and a meteorological condition; generating an environment three-dimensional semantic map of the flight complex scene; constructing an initial global path of the flight unmanned aerial vehicle corresponding to the flight complex scene; establishing a dynamic threat identification model of the flight scene of the unmanned aerial vehicle to identify a sudden threat factor of the flying unmanned aerial vehicle, and analyzing a factor potential movement track of the sudden threat factor; calculating a risk coefficient of the sudden threat factor to the flying unmanned aerial vehicle; and generating a smooth obstacle avoidance trajectory of the flying unmanned aerial vehicle to execute security early warning of the flying unmanned aerial vehicle. The safety of autonomous flight of the unmanned aerial vehicle in a complex environment can be improved.

The invention discloses an agricultural unmanned aerial vehicle operation path planning system and method based on the Internet of Things, and belongs to the field of intelligent agriculture. An initial path planning model is constructed, the spraying width and the overlapping coefficient are imported into the initial path planning model to calculate the path distance, and a fog drop motion model is constructed; introducing the initial diameter and mass of the fog drops into a fog drop motion model to evaluate the fog drop settling velocity, constructing a fog drop evaporation and drift model, introducing the environment data, the fog drop diameter and the settling velocity into the fog drop evaporation and drift model to evaluate the fog drop evaporation effect and the fog drop horizontal drift distance, and constructing an unmanned aerial vehicle flight height and path correction model; settlement fog drop data and real-time spraying width are imported into an unmanned aerial vehicle flight height and path correction model to adjust the flight height and path of the unmanned aerial vehicle, a dose control model is constructed, the pest and disease damage degree of each grid is imported into the dose control model to evaluate the pesticide spraying dose of each grid, and the spraying precision is improved.

The invention discloses a non-uniform sampling scene self-adaptive frequency spectrum completion method based on an unmanned aerial vehicle platform. The method comprises the following steps: identifying an area shielded by a building; simulating the relationship between the signal intensity and the distance change; training a multi-head graph attention network embedded by building constraint to obtain a frequency spectrum completion intelligent model; in an initial sampling stage, a safety area is generated, candidate waypoints are created, and waypoints around each building are connected to form an unmanned aerial vehicle flight path; adopting an inverse distance weighting algorithm or a Kriging algorithm to finish initial completion so as to estimate the approximate position of the radiation source; and simulating deployment of the unmanned aerial vehicle for secondary sampling, carrying out dense sampling around the identified radiation source, then adaptively perfecting a sampling result by using a spectrum completion intelligent model, completing space completion of spectrum data, and constructing a radio frequency spectrogram. According to the method, the spectrum reconstruction performance is remarkably improved, and the collaborative optimization problem of high-precision requirement and low-cost implementation in urban complex scenes is effectively solved.

The invention discloses a multi-rotor unmanned aerial vehicle broken propeller fault rapid diagnosis and fault-tolerant stable control system and method, belongs to the technical field of unmanned aerial vehicle flight control, and aims to solve the problems that traditional broken propeller detection is high in cost and unreliable, power compensation robustness is insufficient and return flight logic is lacked. According to the system and the method, on the basis of a high-order nonlinear observation theory, rapid and accurate identification of the abnormal state of the unmanned aerial vehicle power system is realized by constructing a multi-modal signal prediction and residual evaluation system; the method effectively prevents the out-of-control, rolling and even crash of the unmanned aerial vehicle caused by power asymmetry, solves the problem that a traditional fixed control strategy is difficult to adapt to fault working conditions, and adapts to dynamic changes under complex working conditions such as hovering and cruising. Finally, closed-loop control from fault detection to safe return can be realized, the autonomous safe return capability of the unmanned aerial vehicle under the condition of the fault of the propulsion system is ensured, and the method is suitable for unmanned aerial vehicle application scenes with high reliability requirements, such as logistics transportation and inspection monitoring.