221 results about "Terrain elevation" patented technology

The invention discloses a photovoltaic power station distributed construction management method and system based on load analysis, and the method comprises the steps: outputting a dynamic load intensity thermodynamic diagram according to the terrain elevation data, meteorological historical data and assembly mechanical parameters of a photovoltaic power station planning region; based on the dynamic load strength thermodynamic diagram, generating a construction zoning scheme for wind pressure resistance optimization; outputting a conflict-free construction resource allocation matrix according to the construction partitioning scheme; on the basis of the construction resource allocation matrix, construction parameters are dynamically corrected by adopting an adaptive particle swarm optimization algorithm, and an anti-deformation construction instruction set is generated; and driving the construction machinery to execute operation according to the anti-deformation construction instruction set, and iteratively updating the global load distribution map by using the reinforcement learning model to form a closed-loop construction management link. By utilizing the embodiment of the invention, the balance of construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and controlling closed-loop quality.

The invention provides a digital twin model correction method and system based on machine vision. The method comprises the steps that a comprehensive data set is constructed by collecting sounding data, tidal flow velocity data and unmanned aerial vehicle images, wave disturbance deviation and water surface reflection distortion are eliminated based on geometric feature matching, and a dynamic compensation coefficient is generated in combination with tidal parameters; after time deviation of sounding data is corrected, a dynamic incidence relation between terrain elevation and sediment migration rate is calculated, a digital twinborn model fusing sedimentary layer thickness distribution and tidal flow velocity driven erosion boundary prediction is established, and a visual terrain curved surface is generated; through reverse verification of real-time sediment accumulation characteristics and terrain curved surfaces, migration rate weight parameters are adjusted in a self-adaptive mode, and dynamic optimization of the model is achieved. High-precision dynamic coupling simulation of sea area terrain evolution and sediment migration is achieved, the real-time performance and reliability of island surface change prediction are improved, and coastal zone safety and economic collaborative decision making is supported.

The invention discloses an unmanned aerial vehicle target tracking and intelligent route planning method based on YOLO and DSM, and the method comprises the steps: enabling an unmanned aerial vehicle to collect video streaming, thermal imaging and distance information in real time through a multi-modal sensor, fusing the multi-modal data through Kalman filtering, detecting a target through a YOLOv11 model, and outputting the position, category and confidence information of the target; a DeepSORT algorithm is adopted to continuously track a target, Kalman filtering is combined to predict a target motion trajectory, and a Hungary algorithm is adopted to realize matching of target detection and a tracking trajectory, so that stable tracking in a complex environment is ensured; the flight path of the unmanned aerial vehicle is planned by using terrain elevation information provided by the DSM and combining with an improved A * algorithm, so that the unmanned aerial vehicle is ensured to avoid obstacles and keep continuous tracking of a target in a complex terrain environment; through a reinforcement learning algorithm, the unmanned aerial vehicle can dynamically adjust the route planning according to the motion state of the target and the environment change, and it is ensured that the system can automatically re-plan the route when the target is lost or the environment obstacle is newly added.

The invention relates to the technical field of surveying and mapping data processing, and discloses a surveying and mapping data processing method based on cloud computing, and the method comprises the steps: obtaining topographic elevation, topographic slope and environmental parameters, carrying out the multi-source fusion and feature extraction based on the data, obtaining a preliminary feature set, carrying out the dynamic correction and integrity verification of the preliminary feature set, and obtaining a final feature set; generating a correction feature set; the method comprises the following steps: obtaining an optimized feature set through residual analysis, dividing high-frequency feature cache, constructing a partition feature set, carrying out synchronization verification on partition data, obtaining a consistency feature set, executing multi-thread integration, forming a global integration feature set, completing abnormal region reconstruction and optimization, generating a high-precision feature set, and finally, carrying out parallel task decomposition and accelerated integration to obtain a high-precision feature set. And outputting high-precision surveying and mapping data. The method solves the problem that the scheduling precision is limited under multi-source complex data.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle automatic navigation method and system for power transmission line inspection. Comprising the steps of collecting terrain elevation data and a wind speed fluctuation signal, generating a line corridor boundary elevation distribution diagram, analyzing the influence of a terrain gradient on a conductor sag, and determining a curvature limitation adjustment demand; re-planning the flight path in combination with the obstacle position information, and generating a detection path sequence; calculating a height abrupt change value and an attitude angle deviation value, judging whether an acceleration impact suppression requirement exists or not, and generating an inspection risk score of the vegetation invasion area; determining a cross span distance risk quantitative index, and generating a flight control instruction sequence; and verifying whether the path meets a boundary integrity standard, and generating an automatic navigation path sequence. According to the method, the problems of instability, insufficient coverage and poor safety of an unmanned aerial vehicle routing inspection path under a complex terrain condition are solved, and stable, continuous and efficient automatic generation of the power transmission line routing inspection path is realized.

A design method of a trust region satellite-borne dual-base interference SAR system is provided, relating to the field of radar measurement. Firstly, error sources of the interference SAR system are classified and analyzed, phase errors caused by image decoherence are calculated according to wave positions, and an analytical expression of baseline-related errors is reserved; a maximum phase error caused by imaging processing and an electronic device are estimated; and a baseline error is estimated. Then, the phase errors and the baseline errors are converted into elevation errors through an elevation fuzzy number, thereby obtaining an analytical expression of the elevation errors and the baseline parameters (length and dip angle). With a relative height measurement accuracy index of the system as an optimization object, a feasible solution interval between the baseline length and the baseline dip angle of the system is solved through a trust region algorithm. A minimum value of the baseline length is corrected according to a flying-around safety distance of a dual-satellite formation. According to the method of the present disclosure, the design of the satellite-borne dual-base interference SAR system can be guided, so that the satellite-borne dual-base interference SAR system is ensured to carry out terrain elevation surveying and mapping tasks with high accuracy and high reliability.

The invention discloses a mountainous area wind field downscaling optimization method based on high-precision simulation, and relates to the technical field of meteorological numerical simulation, and the method comprises the steps: obtaining the terrain elevation data and meteorological monitoring data of a target mountainous area, recognizing slope abrupt change points, constructing a spiral sampling path, calculating a slope change value, determining the distance between sampling points, and generating target sampling data; performing orthogonal decomposition on the sampled data to obtain a frequency component, calculating a surface fluctuation coefficient, and constructing a boundary disturbance equation to obtain surface stress distribution; calculating an airflow motion state based on surface stress distribution, solving a vorticity equation to obtain vorticity characteristics of a leeward area, and calculating airflow motion correction parameters; and carrying out downscaling iterative operation on the corrected parameters and the meteorological monitoring data to generate target area wind field data with hectometer-magnitude spatial resolution. The simulation precision of the local wind field under the complex terrain condition is improved.

The invention relates to the technical field of remote sensing measurement, in particular to a power transmission line tree high-risk assessment method and device based on satellite remote sensing and a medium. And based on the result and line data, calibrating a safety distance, combining terrain elevation correction, identifying the tree, calculating the height of the tree and the clearance distance of the line, comparing the clearance distance with a safety standard, dividing high, medium and low risk levels, and generating a high-risk assessment result of the power transmission line tree. According to the method, the vegetation distribution is described by extracting the spectral features of the remote sensing image, the dynamic safety interval is constructed in combination with the position of the power transmission line and the vegetation distance, the model is established by fusing terrain and crown elevation data, clearance parameters are quantitatively calculated to generate the risk level, the complex terrain adaptability and the risk point automatic identification precision are improved, and the early warning reliability is improved. And intelligence and decision-making accuracy of power transmission line inspection are promoted.

A reservoir basin dam line site selection method based on an improved adaptive variable neighborhood search algorithm is realized based on an established dam line site selection optimization target mathematical model, and comprises the following steps: obtaining area DEM data needing site selection, and dividing the area DEM data into four identical sub-areas R1-R4; obtaining an initial parameter of each sub-region boundary, initializing the initial parameter of each sub-region boundary, and generating an initial solution Sol0; the initial solution Sol0 is used as a starting point, an IAVNS algorithm is adopted to solve the optimization target mathematical model, and the optimal solution Solbest of each sub-region is obtained; and outputting a global optimal solution Solbest fullness of the site selection area by using an SHP file, and carrying out visual display on the global optimal solution Solbest fullness. By proposing an IAVNS algorithm and adopting a double-layer collaborative optimization architecture, a dynamic hierarchical variable neighborhood search strategy and a rapid evaluation method based on terrain elevation variance analysis, global exploration, fine local search and multi-target dynamic balance optimization are realized, and fine optimization requirements under complex terrains are met.

The invention discloses a catastrophe rescue robot digital twin control method and system, and belongs to the technical field of robot intelligent control. Comprising a virtual-real mapping module which processes the body state of a robot and catastrophe scene point cloud data in real time, and constructs and synchronously updates a digital twinborn body containing a terrain elevation map at a cloud end; the hierarchical decision-making module is used for making a decision in the digital twinborn body by utilizing a hierarchical reinforcement learning model, and outputting a wheel-leg movement mode and an excavation arm cooperation instruction by a high-level strategy network according to the terrain complexity; and the safety deduction module introduces a safety reinforcement learning mechanism, deduces and verifies the generated action instruction in the virtual environment, and restrains the dangerous action by controlling a barrier function. According to the method, the decision risk of the heavy robot in the unstructured catastrophic terrain can be reduced, the stability of motion control is improved, and a high-safety control strategy is provided for various complex obstacle crossing actions based on simulation prediction.

The invention belongs to the technical field of SPH modeling, and particularly discloses an SPH particle model and surface geometric model automatic generation method and system, and the method comprises the steps: constructing raster data of a terrain elevation model and a source thickness distribution model, carrying out the data standardization processing, and achieving the unification of the spatial reference of the terrain elevation model and the source thickness distribution model; resampling the resolution of the standardized raster data to ensure that the size of the raster unit is matched with the physical characteristics of the particles; generating terrain boundary particles by adopting a hierarchical generation algorithm, and completing material source particle filling; and based on the grid vertex coordinate set, generating a surface geometric model in an STL format, outputting particle coordinates and an STL file meeting the requirements of an SPH solver, and completing automatic generation of the SPH particle model and the surface geometric model. The problems that an existing SPH modeling method is low in processing efficiency, high in data alignment error and insufficient in model surface geometric accuracy are solved.

The invention provides a wind field inversion method and device based on multi-source prior data, equipment and a medium, and the method comprises the steps: building a target function of a three-dimensional fusion wind field based on the multi-source prior data and a fluid mechanics model simulation wind field, carrying out the iterative optimization of the target function, and determining the three-dimensional fusion wind field; constructing an initial deep learning neural network model, taking the three-dimensional fusion wind field as a truth value label, inputting the urban underlying surface features, the terrain elevation and the numerical weather forecast wind field into the deep learning neural network model, and training to obtain a target deep learning neural network model; and inputting the new numerical weather forecast wind field, the urban underlying surface features and the terrain elevation into the target deep learning neural network model, and outputting a refined wind field. According to the technical scheme provided by the embodiment of the invention, through the trained deep learning neural network model, high-precision rapid inversion of the low-altitude wind field is realized without depending on laser radar and ground observation data and depending on prior information such as numerical prediction and terrain.

The embodiment of the invention provides a reservoir capacity dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a reservoir surface feature data set through the unmanned aerial vehicle, determining the boundary of a water area, scanning the bottom of the reservoir, obtaining the underwater terrain elevation and sediment distribution data, and constructing a three-dimensional terrain model; and determining terrain and deposit change data based on the three-dimensional terrain model dynamic change data of the continuous time sequence. Sedimentation space distribution characteristics are obtained through change rate analysis, and the sediment transportation amount and the sedimentation and deposition space rule are determined in combination with the water flow speed. Key driving factors are screened through correlation analysis, a sedimentation high-risk area is determined according to the key driving factors, and the change of sediments in the area is monitored, so that the change of the reservoir capacity is mastered. The reservoir siltation can be accurately and dynamically monitored, the high-risk area and the change mode are defined, the dynamic database is constructed to update the capacity change in real time, and important technical support is provided for reservoir management and flood control and disaster reduction.

The invention discloses a non-flat terrain autonomous navigation method based on path pruning and trajectory smoothing. The method comprises the following steps: constructing an elevation map with three-dimensional map information as a navigation map according to robot navigation space information; based on the navigation map, performing global path search by adopting a path pruning strategy to obtain an initial path from the starting point to the target point; based on the initial path, track optimization is carried out by using a B spline curve, and a smooth track meeting robot dynamics constraints is generated; in response to an obstacle which is detected on the smooth trajectory and cannot be avoided, performing three-dimensional obstacle crossing trajectory planning based on terrain elevation information of the elevation map, and generating an obstacle crossing trajectory capable of crossing the obstacle; wherein the robot performs autonomous navigation based on the smooth track or the obstacle crossing track. According to the invention, efficient global search, smooth trajectory optimization and intelligent obstacle crossing decision of the robot in unknown and non-flat terrains can be realized.

The invention provides a distributed MIMO clutter echo simulation method based on GIS information, and the method comprises the steps: obtaining the GIS information of a geographic information system, and dividing a surface scatterer irradiated by an MIMO radar into a plurality of basic scattering units based on the GIS information; calculating slope distances, Doppler frequencies, terrain elevation values and land cover types of the plurality of basic scattering units to form a scene model element set; mapping a plurality of basic scattering units to a radar resolution unit by combining a backscattering coefficient empirical model and a resolution adaptive grid mapping method according to the scene model element set; dividing the radar resolution unit into a permanent scattering area and a dynamic scattering area according to GIS information; performing clutter statistical characteristic simulation processing on the permanent scattering region and the dynamic scattering region to obtain a clutter amplitude sequence; and performing echo analysis processing on the clutter amplitude sequence in a time-frequency domain to obtain clutter echo simulation data. And the simulation precision of clutter echo simulation data is integrally improved.

The invention discloses a data management system based on unmanned aerial vehicle surveying and mapping, and particularly relates to the technical field of data management. The method comprises the steps of collecting unmanned aerial vehicle surveying and mapping data, and performing space coordinate registration and unified coding; performing spatial region division according to the terrain elevation variable coefficient and the obstacle projection coverage rate to obtain a high-density verification region and a low-density verification region; for the high-density verification area, calculating the feature matching degree of an overlapping area of adjacent frames through a local feature matching algorithm, and outputting first correction data; performing data compensation on the low-density verification region by adopting spatial interpolation and cubic spline interpolation to generate second correction data; generating a full-scene reconstruction data frame based on the first correction data and the second correction data; based on full-scene data frame reconstruction, region weight factor distribution, differential error correction coding execution and unmanned aerial vehicle surveying and mapping result data output, the spatial adaptability of data verification is effectively improved, and the overall accuracy and integrity of unmanned aerial vehicle surveying and mapping data are improved.

The invention provides a construction control system, method and equipment of an underwater leveling machine and the underwater leveling machine, and relates to the technical field of ocean engineering. The construction control system comprises a sensing subsystem used for obtaining the weight of stone in a hopper of the underwater leveling machine and the inclination angle of the hopper; the calculation subsystem is used for determining the stone mass based on the weight of the stone in the hopper and the inclination angle of the hopper and calculating the real-time material distribution mass flow rate based on the time sequence data of the stone mass; the current terrain elevation of the foundation bed is obtained to calculate the terrain residual error and the to-be-laid volume; and the control subsystem is used for generating a cloth control instruction through an optimization decision algorithm based on the real-time cloth mass flow rate, the terrain residual error, the to-be-laid volume and the predicted thickness distribution. According to the system, real-time working condition monitoring, dynamic flow calculation, terrain deviation analysis and predictive control strategies can be combined, closed-loop accurate control over the whole process from material conveying to bottom bed forming is achieved, and the control precision of underwater leveling construction is improved.

The invention relates to the technical field of rainfall runoff simulation, and discloses a BIM-GIS (Building Information Modeling-Geographic Information System) coupling-based rainfall runoff dynamic simulation method, which comprises the following steps of: planning a simulation process based on input parameters and environmental conditions, determining required BIM data (including building geometric data) and GIS data (including terrain elevation data), and determining different scene types; then generating a dynamic simulation process set, determining an execution sequence, and generating a coupling parameter setting list and a data fusion scheme list of each scene; and finally associating the elements to generate an overall scheme. In addition, the invention also relates to the contents of dynamic simulation boundary determination, data subset extraction, scene type subdivision, simulation process updating, data fusion rule and database establishment and maintenance and the like. The method integrates the technical advantages of BIM and GIS, can adapt to different scenes, realizes data dynamic updating and efficient processing, improves the accuracy and reliability of rainwater runoff dynamic simulation, and can be used in the related field of urban rainwater management.

The invention relates to the technical field of fire spreading prediction, and discloses a fire spreading prediction method and system based on local real-time high-precision weather prediction, a terminal and a storage medium, and the method comprises the steps: obtaining fire observation data collected by a plurality of pieces of information collection equipment for a target region, preprocessing the fire observation data through an edge calculation layer to obtain target fire observation data; a lightweight 3D-Transform architecture is designed based on a big archaeological meteorological model, and the target fire observation data and historical fire scene case data are fused to generate a meteorological prediction result; and constructing a fire spread prediction network model, jointly inputting the meteorological prediction result, the terrain elevation and the fuel humidity, and outputting a fire spread probability graph and a spread velocity field in a future preset time. According to the method, the fire spreading prediction precision and speed are improved, and a high-precision and high-response full-chain solution is provided for forest fire emergency management.

The invention relates to the technical field of power grid planning and analysis systems, in particular to an online power grid planning and analysis system combined with a remote sensing map, which comprises a multi-source remote sensing data fusion module, a multi-source remote sensing data fusion module and a data processing module, generating a three-dimensional terrain model based on the satellite remote sensing image, the laser radar point cloud data and the terrain elevation data; and the power grid constraint region extraction module is used for performing power grid sensitive region identification on the three-dimensional terrain model. Through multi-source data fusion, power grid load prediction accuracy is improved, multi-target optimization is realized by using an algorithm, construction cost, power supply reliability and environmental influence are effectively balanced, power grid resource allocation efficiency is improved, and through the risk assessment module, high-risk power grid nodes are accurately identified, protection measures are taken in advance, power grid disaster resistance is enhanced, and power grid resource allocation efficiency is improved. Disaster loss is reduced, and power grid planning quality, operation efficiency and reliability are substantially improved on the whole.

The invention belongs to the technical field of pipeline flow monitoring, and particularly relates to a long-distance water delivery pipeline flow monitoring method fused with terrain dynamic compensation, which comprises the following steps: selecting a long-distance water delivery pipeline reference point, and synchronously acquiring reference flow and static pressure; based on a multi-source data fusion technology, constructing a pipeline terrain elevation parameter model, generating a three-dimensional terrain matrix, calculating an elevation difference, a gradient and a curvature radius, and constructing a terrain correction matrix; on the basis of Darcy formula correction, down-slope section kinetic energy suppression and a down-slope section pressure compensation algorithm, head loss is corrected, potential energy conversion errors are suppressed, and water hammer effect interference is eliminated; the flow in the pipeline is corrected through edge calculation, and global flow balance verification is carried out; and pipe bursting or leakage points are positioned in an auxiliary manner based on an integrated visual platform. According to the method, the measurement precision of the pipeline flow under the complex terrain is effectively improved, and a high-precision and all-terrain self-adaptive intelligent monitoring and early warning platform is provided for a long-distance water conveying pipeline.

The invention relates to the technical field of computer graphics, and particularly discloses a three-dimensional intelligent visualization method and device for multi-source data loading, a medium and equipment.The method comprises the steps that an oblique photography model is loaded and dynamically adjusted, and an oblique photography live-action base is constructed; generating semantic 3D tiles data on the basis of the base; identifying a key entity object, carrying out lightweight processing on a high-precision GLB / GLTF model of the key entity object, and dynamically implanting the high-precision GLB / GLTF model into a scene; performing dynamic analysis and spatial superposition on the KML data associated with the key object, and constructing a multi-level three-dimensional scene information system from entity annotation to macroscopic annotation; dynamically coupling the KML elements with terrain elevation data to form a high-precision digital elevation base supporting visualization; based on a video memory dynamic partitioning mechanism, optimized resource scheduling is performed on a substrate, and adaptive balance of terrain complexity and system load is realized. According to the invention, the precision and efficiency of multi-source heterogeneous data fusion and the system stability can be improved.

The invention discloses a PSO-improved three-dimensional sensor network optimization deployment method. The scheme comprises the following steps: constructing a three-dimensional space monitoring model fusing terrain elevation and obstacle distribution; designing a dynamic multi-target fitness function fusing the space coverage rate, the energy consumption balance degree and the obstacle penetration loss coefficient; dynamically adjusting the search weight of the particle swarm in the Z-axis direction according to the terrain gradient; dividing a three-dimensional space into sub-regions and initializing a particle swarm; and iteratively optimizing the node position by adopting an improved PSO algorithm until a termination condition is met. The method has the advantages that the method is suitable for scenes such as mountain monitoring, unmanned aerial vehicle cluster networking and ocean three-dimensional monitoring, the three-dimensional coverage rate can be increased by 15%-30%, the network service life can be prolonged by 20% or above, real-time node redeployment in a dynamic environment is supported, and through innovative three-dimensional space modeling, a multi-target optimization algorithm and a parallel computing architecture, the real-time node redeployment can be realized. And comprehensive improvement of the deployment quality and efficiency of the sensor network in a complex environment is realized.

The invention discloses a flood disaster assessment method and system based on satellite remote sensing data, and relates to the technical field of flood assessment, and the method comprises the steps: distinguishing different earth surface types of a monitoring region, collecting remote sensing data, and obtaining hydrological information, historical flood data and terrain elevation information; and dynamically updating the data updating frequency and precision of the monitoring area according to the flood risk of the area. According to the method, the difference value between the layers is calculated through the layered pyramid data, high-frequency detail information of the data can be effectively extracted and reserved, the space complexity of the data of each layer is quantized, the fusion weight of the data of each layer is dynamically adjusted, the multi-source data is reasonably fused under different scales and feature complexity, and the fusion efficiency is improved. The actual feature complexity of the data is reflected by introducing the data variability, the limitation of a simple weight distribution method is avoided, highly-adaptive multi-scale data fusion is realized, and the change of the water depth and the water velocity in different directions can be comprehensively considered through a three-dimensional shallow water equation model.

The invention relates to a specific scene elevation fine construction method based on multi-source heterogeneous probability fusion. The method comprises the following steps: synchronously acquiring multi-source data through an RGB sensor, a binocular infrared depth sensor and an inertial measurement unit; performing instance segmentation and depth aggregation on the RGB color image to obtain a scene instance segmentation result and first depth data; reconstructing an initial point cloud based on the binocular infrared depth image; carrying out pose calculation through a vision-inertia odometer, coupling loopback detection to optimize a camera track, and outputting a global consistent pose; fusing the first depth data with an initial point cloud to generate a segmentation point cloud with instance information; and finally, generating a cross-modal enhanced fusion point cloud based on the segmentation point cloud, performing multi-source heterogeneous probability fusion and Kalman filtering in combination with a global consistent pose, and outputting a decoupling elevation fine composition capable of distinguishing a low object scene. According to the method, the understanding of a scene environment is effectively enhanced, and the intelligence level of terrain elevation data generation is remarkably improved.

The invention relates to the technical field of satellite navigation terminals, and discloses a Beidou terminal capable of intelligently evaluating multiple states. Comprising a Beidou positioning module used for acquiring a Beidou signal corresponding to the Beidou terminal; the control module is configured to execute the following steps: performing time sequence modeling on the propagation time delay, the geometric distribution factor, the ionosphere influence parameter and the troposphere influence parameter of the Beidou signal to generate a positioning reliability index so as to realize positioning state evaluation; performing conjoint analysis on the Beidou signal, and outputting a data credibility grade; processing the inertial navigation data, the terrain elevation data, the sky satellite geometric distribution factor and the multi-terminal altitude difference data corresponding to the Beidou terminal through a convolutional neural network to generate a three-dimensional environment interference coefficient; and fusing the channel fading parameter, the network topology data, the communication antenna signal-to-noise ratio, the transmission rate change parameter and the data integrity parameter corresponding to the Beidou terminal, and calculating a communication link stable value to realize communication state evaluation.

The invention provides an agricultural unmanned aerial vehicle intelligent detection and early warning method and system based on image recognition, and relates to the technical field of agricultural engineering, and the method comprises the steps: setting an initial inspection route and image collection parameters of an unmanned aerial vehicle through a mobile terminal; according to the initial inspection route and the image acquisition parameters, planning a plurality of sampling areas and acquiring topographic elevation data, and processing topographic relief features based on the elevation data; carrying out fusion calculation on the topographic relief features and the spatial configuration of a preset track planning area, outputting a route adaptive adjustment coefficient, and reconstructing the initial inspection route based on the adjustment coefficient to obtain a topographic adaptive optimized route; and controlling the unmanned aerial vehicle to autonomously fly along the terrain adaptive optimized route, and collecting crop canopy multispectral image data in real time through a carried multispectral imaging sensor. The requirement of large-scale agricultural production for disease and pest monitoring timeliness is met.

The invention provides an electric power emergency unmanned aerial vehicle disconnection phased array auxiliary return flight planning method and system, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: enabling an unmanned aerial vehicle to obtain and analyze a signal through an airborne signal receiving unit based on a directional navigation signal, so as to obtain navigation data; calculating the current actual position of the unmanned aerial vehicle according to the navigation data in combination with inertial measurement unit information and terrain elevation data built in the unmanned aerial vehicle; and taking the safe landing preparation point and a dynamic reference point as focuses, constructing a dynamic navigation constraint ellipse according to a preset eccentricity, and calculating the orientation and the in-domain course of the unmanned aerial vehicle relative to the dynamic navigation constraint ellipse. According to the method, phased array directional navigation signals are combined with dynamic path planning, so that the safety and adaptability of autonomous homeward voyage of the unmanned aerial vehicle in a complex terrain environment are improved.

The invention discloses a rapid downscaling calculation method for meteorological wind field data based on a diffusion model and an attention mechanism, and solves the problems that a non-physical vortex structure appears in a generated high-precision wind field due to a mode collapse phenomenon in the prior art, and multi-scale dynamic association between atmospheric variables and topographic features cannot be established. The method specifically comprises the following steps: step 1, acquiring a data sample set comprising low-resolution meteorological wind field data and terrain elevation data, and dividing the data sample set into a training set and a test set; 2, constructing an initial target diffusion model based on a UNet network, wherein the initial target diffusion model comprises a noise prediction network; 3, training the noise prediction network of the initial target diffusion model by using the training set, and constructing a final target diffusion model; 4, testing the final target diffusion model by using the test set to determine a qualified final target diffusion model; and 5, carrying out fast downscaling calculation on meteorological wind field data through the qualified final target diffusion model.

A mower slope sensing system on a grass mowing machine. An elevation sensor may capture terrain elevation values for each of a plurality of different locations according to a latitude and a longitude of each of the locations on a field. An electronic controller may store the terrain elevation values, latitudes and longitudes, and adjust a mowing blade speed depending on changes in the terrain elevation values from a current location to a next expected location.