596 results about "Flight altitude" 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 an unmanned aerial vehicle flight control method and system based on a target image, and relates to the field of unmanned aerial vehicle flight control, and the method comprises the steps: obtaining the relative position of an unmanned aerial vehicle in a flight region based on the target image and a position calculation algorithm, and judging the yaw state of the unmanned aerial vehicle through a position comparison method; analyzing the target image by using an identification processing technology based on yaw state judgment to determine a flight yaw angle of the unmanned aerial vehicle, and generating a yaw control quantity according to the flight yaw angle to control the flight state of the unmanned aerial vehicle; collecting a real-time target image of the unmanned aerial vehicle after the flight state is adjusted, analyzing the flight height of the unmanned aerial vehicle according to the real-time target image, and planning a landing track of the unmanned aerial vehicle based on the flight height; and generating an autonomous landing control instruction according to the unmanned aerial vehicle landing trajectory and a preset flight demand to complete autonomous landing flight control of the unmanned aerial vehicle. By collecting the target image in real time and analyzing the flight height, the landing track of the unmanned aerial vehicle can be accurately planned.

The invention provides a low-altitude airspace dynamic decision-making system based on a multi-modal large model, and relates to the technical field of low-altitude airspace traffic management.The low-altitude airspace dynamic decision-making system is characterized in that a first thermal disturbance coverage map, a second shear wind high-risk area map, a third turbulence risk drainage basin map and a fourth sudden obstacle prediction area map are generated by a map construction module; the aircraft can judge whether the aircraft enters the dangerous area in real time and assess the safety of the flight path according to the superposed risk value. When the superposition risk value # imgabs0 # of the flight path section passing through the jth area in the first original flight path of the ith aircraft is in a middle risk section, the system generates a first-level avoidance strategy, and the height, the speed and the course of the aircraft are adjusted; and when the superposed risk value # imgabs1 # is a high-risk section, the system generates a secondary avoidance strategy, and bypasses a high-risk area by remarkably increasing the flight height and reducing the speed.

The present invention relates to the field of mine surveying and mapping technology, and in particular to a mine surveying and mapping method and system based on unmanned aerial vehicle remote sensing technology, the method comprising: obtaining terrain data of a mine; generating a flight trajectory of an unmanned aerial vehicle; using an unmanned aerial vehicle to collect image data and hyperspectral data of the mine; constructing a three-dimensional surface model of the mine; obtaining geological structure data and mineral distribution data of the mine; and generating a three-dimensional structural model of the mine based on the geological structure data, mineral distribution data and the three-dimensional surface model. The present invention generates a flight trajectory of an unmanned aerial vehicle to ensure that the unmanned aerial vehicle takes photos at a reasonable flight altitude and speed in different areas, thereby reducing repeated coverage and maximizing regional coverage. By generating a three-dimensional structural model, detailed geological structure and mineral distribution data can be obtained without actually entering the mining area, greatly reducing the safety risks and economic costs that may be caused by on-site surveys.

The invention relates to the technical field of multi-screen display, in particular to a multi-screen display picture control method and system for an unmanned aerial vehicle, and the method comprises the following steps: comparing the flight height with an obstacle position parameter according to the flight path planning data of the unmanned aerial vehicle and real-time obstacle information; and performing path segmentation calculation of adjacent areas by combining the speed and the flight direction of the unmanned aerial vehicle, evaluating the minimum safety distance of the obstacle, and outputting a path planning safety coefficient. According to the invention, through multi-parameter dynamic comparison of the flight height, the obstacle position, the speed and the direction, the flexibility of path adjustment and the obstacle avoidance precision are improved, and the dynamic distribution of the flight picture and state information is combined with the view angle switching position parameters, so that efficient distribution of multiple devices is realized; real-time environment data processing is combined with image frame feature extraction and region division scheduling, navigation data priority distribution is optimized, information synchronization is optimized through path overlapping parameters and communication delay, and multi-machine cooperation efficiency and task execution reliability are improved.

The invention discloses a substation unmanned aerial vehicle middle and low altitude automatic flight patrol safety management and control method, and relates to the technical field of intelligent operation and maintenance of power system equipment, and the method comprises the steps: setting a plurality of micro radar signal sources in a substation, and testing the signal intensity of a wireless communication system; dividing an interference area through the three-dimensional model of the transformer substation, recording the coordinates of the area causing the abnormity of the unmanned aerial vehicle equipment, and setting the area as a flight forbidden area; and generating an unmanned aerial vehicle flight path based on the transformer substation three-dimensional model, and setting a corresponding flight height and an electrified equipment distance according to different voltage class sites. According to the invention, double-AP module distributed deployment and multi-direction obstacle avoidance module configuration are adopted, so that the reliability of a communication link and the flight safety are guaranteed; based on interference area identification and differentiated parameter setting of a three-dimensional model, a precise control mechanism for different voltage levels is established, and the reliability and operation efficiency of automatic inspection of the unmanned aerial vehicle are improved.

The invention relates to the field of unmanned aerial vehicle control, and discloses a police unmanned aerial vehicle management and control system for handling emergencies, which comprises a sensing module, a distribution module, a planning module, a control module, a tracking module and a feedback module. The sensing module is used for collecting environment data in real time and generating a three-dimensional environment model; the distribution module optimizes an unmanned aerial vehicle task distribution scheme; the planning module dynamically plans the optimal flight path of the unmanned aerial vehicle according to the task allocation result and the three-dimensional environment model; the control module dynamically adjusts the attitude and flight height of the unmanned aerial vehicle; the tracking module tracks the target position in real time and predicts the motion trail of the target; and the feedback module transmits the execution condition of the unmanned aerial vehicle, the target tracking result and the field real-time picture back to the command center. Through cooperative work of all the modules, rapid response and efficient disposal of emergencies are achieved, the system is particularly suitable for police service tasks in complex and dynamic environments, and the system has the advantages of being high in response efficiency, high in environmental adaptability and high in resource utilization rate.

The invention discloses a dense small target detection method for an unmanned aerial vehicle aerial photography scene, and belongs to the technical field of computer vision and target detection. In order to solve the problems of small target scale dynamic change and feature expression weakening caused by flight height change, imaging resolution difference and scene complexity in aerial photography of an unmanned aerial vehicle, the invention provides a detection framework fusing an attention scale selection (AGSS) module and a dynamic local self-attention (DPSA) module. The method specifically comprises the following improvements: (1) an AGSS module enhances the significance and discrimination ability of small targets in multi-scale features through global context modeling and a dynamic weight distribution mechanism; and (2) a DPSA module introduces a sparse selection mechanism in a channel dimension, and focuses computing resources on a channel sensitive to a small target, so that efficient and lightweight attention modeling is realized. The above modules cooperate with each other, so that high reasoning efficiency is maintained, and small target detection precision and robustness in a complex background, low illumination and dense target scene are significantly improved. Experimental results show that on typical unmanned aerial vehicle aerial photography data sets such as VisDrone-DET2019 and the like, the method is superior to an existing mainstream method in multiple indexes such as the average precision (mAP), the accuracy rate and the recall rate, especially has obvious advantages in the aspects of integrity and stability of small target detection, and has good practical application value and popularization prospects.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention provides an unmanned aerial vehicle testing method, device and equipment, belongs to the technical field of unmanned aerial vehicles, and solves the problems that a traditional unmanned aerial vehicle is low in testing efficiency, long in time consumption and high in testing cost, and the accuracy is difficult to guarantee. The method comprises the steps of generating a test instruction and sending the test instruction to the unmanned aerial vehicle; acquiring flight state data of the unmanned aerial vehicle; processing the flight state data to obtain performance data, the performance data including at least one of endurance time, a climbing rate, a maximum voyage, a maximum flight height and a maximum flight speed; and inputting the flight state data and the performance data into a flight state test model for processing to obtain test data. According to the scheme, the testing efficiency and accuracy of the unmanned aerial vehicle are improved, and the testing cost is reduced.

The invention provides a target aircraft carbon emission determination system, and relates to the technical field of aircraft carbon emission determination. The method comprises the steps that the flight duration T of a target aircraft on a target flight path, the take-off weight of the target aircraft and the flight height and the flight speed of the target aircraft in the level flight stage on the target flight path are acquired, and the flight duration T of the target aircraft on the target flight path is calculated according to the model of the target aircraft; determining a carbon emission calculation weight corresponding to the target aircraft from a preset carbon emission calculation weight mapping table so as to determine the carbon emissions of the climbing stage, the level flight stage and the landing stage, and further obtaining the corresponding carbon emission of the target aircraft flying on the target flight path; according to the invention, the determined carbon emission corresponding to the flight of the target aircraft on the target flight path is more accurate.

The invention provides an unmanned aerial vehicle positioning method and device, equipment and a storage medium, relates to the technical field of communication, and is used for solving the problem of how to improve the accuracy of unmanned aerial vehicle positioning. The method comprises the steps that a high-altitude platform unmanned aerial vehicle sends a positioning signal to a target node, wherein the target node is located in a signal coverage area of the high-altitude platform unmanned aerial vehicle; then, the high-altitude platform unmanned aerial vehicle obtains multiple pieces of position information and multiple target durations, one piece of position information corresponds to one target duration, the position information is used for indicating the position when the low-altitude platform unmanned aerial vehicle receives a first reflection signal, and the first reflection signal is sent by the target node in response to the positioning signal; the target duration is the transmission duration of the first reflection signal reaching the low-altitude platform unmanned aerial vehicle, and the flight height between the low-altitude platform unmanned aerial vehicle and the ground is smaller than the flight height between the high-altitude platform unmanned aerial vehicle and the ground. And finally, the high-altitude platform unmanned aerial vehicle determines the position information of the target node according to the multiple pieces of position information and the multiple target durations.

The invention, which relates to the technical field of agricultural intelligent equipment, discloses an agricultural plant protection unmanned aerial vehicle sowing control system comprising a sowing flow field dynamic modeling and prediction module, an attitude regulation and control module, a speed matching module, a path compensation module, a visual monitoring module and a control strategy module. A flow field dynamic mapping model based on the flight height, the downward pressing airflow speed and the particle size and density of the scattered particles is constructed, particle drop point deviation is predicted in combination with flight real-time data, and a pneumatic coupling risk area is identified. Through flow field modeling prediction and attitude adjustment, trajectory compensation, speed control and visual feedback, adaptive regulation and control in the sowing process are realized, air scattering and nonlinear collapse are effectively prevented, seedling burning, germination failure and pesticide residue risks caused by over-dense sowing are avoided, the operation uniformity and the agricultural effect are improved, and the method has practical value and popularization prospects.

The invention discloses an off-line and on-line combined unmanned aerial vehicle simulation flight path planning method, which belongs to the field of unmanned aerial vehicle flight path planning, and specifically comprises the following steps: obtaining a series of insertion waypoint longitudes and latitudes through a waypoint interpolation algorithm according to a selected route before a flight task is executed in an off-line mode; then altitude information stored in an elevation database is obtained in an elevation query mode, the target height of each waypoint of the unmanned aerial vehicle is calculated, and then route planning in the whole task process is completed; in the online mode, in the flight mission execution process, a real-time to-ground laser ranging system is adopted to obtain real-time to-ground height information of the unmanned aerial vehicle, after a measurement error is calibrated by combining flight attitude information of the unmanned aerial vehicle, the measurement error is compared with the actual flight height of the unmanned aerial vehicle, and the to-ground height of the unmanned aerial vehicle is calibrated on the basis of a pre-planned route; and the ground-imitating flight precision of the unmanned aerial vehicle is improved. According to the invention, high-precision ground-imitating equal-altitude flight route planning of the long-endurance unmanned aerial vehicle in a complex environment is realized.

The invention discloses a power line inspection unmanned aerial vehicle pose estimation method and device based on multi-modal data fusion, and the method comprises the steps: obtaining detection data of a binocular vision sensor, an IMU and a TOF sensor, and obtaining multi-modal detection data; using TOF sensor data to initialize a flight height and performing scale alignment with a speedometer; determining a linear velocity reference value and an angular velocity reference value, and calculating adaptive weights of the binocular vision sensor and the IMU; according to the binocular vision sensor, the detection data of the IMU and the calculated adaptive weight, performing pose estimation based on an adaptive extended Kalman filtering algorithm to obtain a pose estimation result of the binocular IMU odometer; and fusing the pose estimation results of the monocular odometer and the binocular IMU odometer to obtain the final pose of the unmanned aerial vehicle. According to the method, the pose estimation precision, the environment adaptability and the robustness in the electric power line inspection process can be improved.

The invention discloses a low-altitude airspace channel setting method for an urban air traffic scene, and relates to the technical field of airspace channels, and the method comprises the steps: collecting the operation data of an aircraft in real time, subdividing the flight height layer of the aircraft, and preliminarily dividing the urban airspace; the method comprises the following steps: forming aircraft trajectory sequence data based on trajectory data, performing trajectory sequence point mapping, calculating the similarity of aircraft trajectories by using a DTW algorithm, and performing clustering analysis on DTW similarity data by using a density peak clustering DPC algorithm. According to the method, density analysis is carried out on a DTW similarity matrix through DPC clustering, a density peak value in a track is found to serve as a clustering center, a highly flexible clustering mechanism is formed, efficient division of the airspace is achieved through further airspace division, and the clustering accuracy is improved based on a clustering analysis result and input state space data. The DDPG model can dynamically predict the maximum number of aircrafts which can be accommodated in the current airspace and optimize the real-time path planning of the aircrafts.

The invention discloses a rice yield estimation method based on unmanned aerial vehicle remote sensing and rice spike feature extraction, which comprises the following steps: firstly, assuming a ground section RTK base station and combining an unmanned aerial vehicle remote sensing platform to determine a coverage range and a spatial position of a rice region, and then collecting a rice image and canopy height and performing deblurring processing; then using a rice ear detection model to obtain a rice ear segmentation mask, determining a pixel scaling factor to construct a rice ear feature index, and carrying out statistical analysis and identification on key features to form a feature data set; performing yield estimation by using a rice yield estimation model, and optimizing the model in combination with the actual yield; and finally, transmitting the data to a ground end to generate a thermodynamic diagram and a statistical diagram, and carrying out regional comparison and time sequence trend analysis. According to the method, the influence of the flight height on yield estimation is eliminated based on the rice ear segmentation result in combination with high-precision depth information, and the accuracy and efficiency of rice yield estimation are effectively improved.

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 provides an adjustable air-breathing power ultra-low orbit satellite configuration. The adjustable air-breathing power ultra-low orbit satellite configuration comprises an adjustable closed head part, a body, a stable wing surface and a contraction tail part, the adjustable closing head and the contraction tail are connected to the two opposite ends of the body respectively, and the stable wing face is connected to the side, close to the contraction tail, of the body. The design that the adjustable closing head is an openable / closable head is adopted, the adjustable closing head is a spherical head after being closed when a satellite does not need to inhale, and resistance is reduced; and after being expanded, the air inlet channel can be contracted into an inwards-concave air inlet channel, so that active compression of air is achieved. Therefore, task requirements under different flight heights can be met through flexible switching of the inspiration working mode and the non-inspiration working mode, the aerodynamic resistance of the satellite in an ultra-low orbit is effectively reduced, and the hang time of the satellite is prolonged.

A flight path generation system for generating a three-dimensional flight path for an unmanned aerial vehicle includes an acquisition device to acquire route data including information of a two-dimensional flight path of the unmanned aerial vehicle and map data including information of elevation at each point on the two-dimensional flight path, and a processor configured or programmed to generate the three-dimensional flight path by determining a flight altitude of the unmanned aerial vehicle from a ground on the two-dimensional flight path based on the two-dimensional flight path and the elevation. The processor is configured or programmed to determine the flight altitude in an excess region with an altitude not exceeding a first altitude and a first region with an altitude not exceeding the first altitude, such that a maximum value of the flight altitude of the unmanned aerial vehicle in the excess region does not exceed the first altitude.

The invention relates to the field of aircrafts, in particular to a fuel cell system control method and device, a storage medium and electronic equipment. The fuel cell system control method comprises the following steps: acquiring flight parameters of an aircraft, wherein the flight parameters comprise the flight altitude of the aircraft; determining a target flight condition of the aircraft according to the flight parameters; calculating electric pile required power of a fuel cell system of the aircraft under the target flight working condition; and controlling the fuel cell system to provide power support for the aircraft based on the stack demand power. Through the method, the fuel cell system of the aircraft can better adapt to power requirements under different flight working conditions, and can provide stable and sufficient power support for the aircraft, so that the performance stability and reliability of the aircraft are improved.

The invention discloses an unmanned aerial vehicle channel inspection path optimization method based on dynamic environment perception, and relates to the technical field of unmanned aerial vehicle intelligent path planning. Comprising the following steps: establishing a three-dimensional environment model based on channel geographic information data and surveying and mapping point cloud data, and generating an initial routing inspection path set with a coverage range meeting requirements; predicting and generating an obstacle envelope sequence changing along with time by using a dynamic obstacle trajectory collected by an airborne radar and a camera, performing space-time superposition calculation on the obstacle envelope sequence and the initial inspection path, and removing path fragments conflicting with a future obstacle movement range to obtain an inspection path set after obstacle avoidance correction; and according to elevation information in the three-dimensional environment model, predicting a local area and a spatial range in which communication shielding may occur in the inspection path of the unmanned aerial vehicle, carrying out secondary correction on a path segment which is about to enter a communication shielding area, and outputting a corresponding flight height and speed sequence.

The invention discloses an artificial intelligence inspection system and method based on an unmanned aerial vehicle, and particularly relates to the technical field of artificial intelligence of the unmanned aerial vehicle. The method comprises the following steps: acquiring magnetic field interference parameters and terrain complex parameters in pipeline inspection paths in real time, extracting magnetic field intensity fluctuation characteristics and obstacle density abnormal change characteristics, calculating navigation and positioning system accuracy weight assignment of each path in combination with machine learning methods such as polynomial regression, and generating a comprehensive inspection accuracy index; the comprehensive accuracy index is compared with a gradient standard threshold value, so that the accuracy division of the inspection path is realized: for incomplete accuracy inspection, the accuracy abnormal degree of a future navigation system is predicted, and if necessary, the flight height is adjusted and an obstacle dense area is avoided; the problems of false detection and missing detection caused by magnetic field interference and complex terrain in the prior art are effectively solved, the efficiency, accuracy and reliability of the unmanned aerial vehicle inspection task are remarkably improved, and major disasters caused by the fact that leakage is not detected in time are avoided.

The invention discloses an intelligent navigation method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the following steps: acquiring point cloud data and motion data, and calibrating to obtain a relative position and time synchronization parameter; performing map construction on the point cloud data and the motion data to generate a three-dimensional point cloud map; analyzing the boundary coordinates of the flight area, the flight height and the route step length, and generating route planning parameters; the boundary of the target inspection area is defined by combining the three-dimensional point cloud map, and an optimized route and corresponding waypoint sequence data are generated through route planning; acquiring real-time pose data of the unmanned aerial vehicle, comparing the current position with the waypoint sequence, and triggering image acquisition when a preset waypoint reaches a tolerance range; inspection data is obtained; and integrating the real-time pose data, the three-dimensional point cloud map, the flight state and the inspection data to form an intelligent navigation route. According to the invention, high-precision positioning and linkage of waypoints and data acquisition are realized, and the navigation precision, the inspection efficiency and the operation safety are improved.

The invention discloses an unmanned aerial vehicle position and sensor power joint optimization method and device and a storage medium, and the method comprises the steps: building an energy efficiency optimal mathematical optimization model considering the constraints of data safety, maximum power, flight height and the like through defining the confidential transmission energy utilization efficiency of data collection; meanwhile, a problem solving process is given; therefore, joint optimization of the real-time position of the unmanned aerial vehicle and the sending power of the sensor is realized. Through joint optimization of the real-time position of the unmanned aerial vehicle and the sending power of the sensor, the safety of sensor data acquisition is ensured, and the energy utilization efficiency of the sensor is improved as much as possible.

The invention discloses an unmanned aerial vehicle countering method and system based on deep learning, and relates to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: collecting unmanned aerial vehicle data, and carrying out the data preprocessing; constructing a first model, and optimizing the model; and analyzing the illegal unmanned aerial vehicle through a first algorithm, and making a countering strategy. According to the unmanned aerial vehicle countering method based on deep learning, the generalization ability and robustness of the model are improved through unmanned aerial vehicle image and video data acquisition, the training efficiency and accuracy of the deep learning model are improved through preprocessing the acquired data, and the accuracy of the deep learning model is improved through rule setting of the flight height, speed, course and other features. According to the unmanned aerial vehicle countering method, the capability of discovering illegal unmanned aerial vehicles is enhanced, the countering strategy is formulated by combining the classification result, the effectiveness and flexibility of unmanned aerial vehicle countering are improved, and better effects are achieved in the aspects of robustness, accuracy and effectiveness.

The invention relates to the technical field of real estate surveying and mapping, and particularly discloses a real estate surveying and mapping project regional surveying and mapping method and system based on an unmanned aerial vehicle, and the method comprises the steps: 1, obtaining basic topographic data, local policies and airspace limitation of a surveying and mapping region, and organizing team personnel to carry out on-site survey; 2, dividing a surveying and mapping area into a plurality of areas by using a GIS tool, formulating a flight height, a flight route and an overlapping rate, and meanwhile, arranging a GCP; step 3, according to the planned flight task, performing unmanned aerial vehicle flight in different regions, and collecting region images and point cloud data in real time; according to the method, the unmanned aerial vehicle is used for carrying the laser measurement system and the camera to collect the point cloud data and the image data of the project surveying and mapping area under the regional condition, and then the point cloud data and the influence data are integrated for drawing to obtain the final surveying and mapping result, so that the field workload can be reduced on the premise of ensuring the result precision; object boundaries are presented in the form of laser points, which is convenient for operators to judge.

The invention belongs to the technical field of induced polarization detection, and provides an air-ground capacitance non-contact frequency spectrum induced polarization detection system and method.The air-ground capacitance non-contact frequency spectrum induced polarization detection method is characterized in that a plurality of power supply electrodes are arranged at intervals along a detection route, and a capacitance plate acquisition module is carried on an unmanned aerial vehicle flight module; the unmanned aerial vehicle flight module and the mobile platform unit are controlled to run along the detection route; an excitation signal with preset amplitude, current, voltage or / and frequency is sent to the power supply electrode, and feedback data generated by the capacitor plate acquisition module is obtained; calculating an optimal detection flight height according to the feedback data; and after the detection tasks of all the measuring points are completed, performing comprehensive processing and imaging inversion on the collected full-band data, generating an apparent resistivity distribution map and a polarization parameter profile map of the underground medium, and obtaining electrical information of the earth surface. According to the invention, the electrical information of the earth surface and the shallow layer in a large area can be rapidly obtained, and the method is suitable for high-efficiency induced polarization detection under various topographic conditions.

The invention discloses an outdoor field large depression angle measurement method and system, and the method comprises the steps: S1, determining the position of a measured target, erecting a calibration ball and a positioning ball, determining the flight height of an unmanned plane, and setting a scanning path; s2, establishing a coordinate relationship among the calibration ball, the positioning ball and the unmanned aerial vehicle; s3, testing the calibration ball and the positioning ball in different directions, and acquiring frequency sweeping echo frequency domain data in different directions; s4, removing the calibration ball, and erecting a measured target at the position of the calibration ball; s5, testing the positioning ball and the tested target in different orientations, and acquiring frequency-sweeping echo frequency domain data in different orientations; and S6, performing phase compensation on the measurement data through theoretical values of different distances from the positioning ball to the radar on the measurement path, and obtaining two-dimensional imaging data and an RCS result of the measured target. The method is suitable for outdoor field measurement, target scattering characteristic diagnosis under complex environments such as large depression angle ground and sea can be realized, flexible deployment requirements can be realized, and the test cost is reduced.

The invention discloses an emergency communication high-altitude base station system for an unmanned aerial vehicle, which relates to the technical field of communication and comprises an unmanned aerial vehicle platform, an intelligent flight control module, a communication management module, a multi-unmanned aerial vehicle cooperative technology module, a data analysis and decision support module, an encryption and security authentication module and a modular design structure. And the remote monitoring and maintenance module is used for maintaining the service system module. The intelligent flight control module integrates functions of flight strategy optimization, automatic obstacle avoidance and the like, can automatically adjust the flight height, angle and path of the unmanned aerial vehicle according to communication coverage requirements, and reduces the complexity and risk of manual operation; the multi-unmanned aerial vehicle cooperation technology module can realize information sharing, task cooperation and optimal distribution of communication resources among multiple unmanned aerial vehicles to form a distributed communication network, thereby not only improving the communication coverage and capacity, but also enabling the resource utilization to be more efficient.