559 results about "Elevation data" patented technology

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy earthwork dynamic monitoring method and system based on multi-source point cloud data fusion, and the method comprises the steps: carrying out the identification and standardization processing of airborne LiDAR, ground scanning and oblique photography point clouds, and generating a standard point cloud data set with uniform spatial positions and balanced density; dividing an abrupt slope area, a gentle slope area and a flat area based on topographic gradients, and constructing a grid framework adaptive to topographic features; carrying out weighted fusion by combining the point cloud coverage rate and the normalized terrain gradient to generate high-precision grid elevation data; calculating an earthwork variation distribution matrix by comparing historical elevation models; dynamically analyzing the change trend of the excavation and filling area, and synchronously evaluating the construction progress deviation and the geological disaster risk. According to the method, the problems of insufficient multi-source point cloud fusion precision, poor terrain adaptability and dynamic evaluation splitting in the prior art can be solved, and the monitoring precision and the engineering safety early warning efficiency under the complex terrain condition are remarkably improved.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention discloses an overhead transmission line mechanical construction three-map one-table intelligent generation method, which comprises the following steps of: fusing a digital orthoimage model, elevation data and a power transmission and transformation project model through a coordinate transformation matrix, and establishing a three-dimensional scene with a terrain curvature attribute; based on the terrain curvature and equipment coordinates of the three-dimensional scene, a multi-objective optimization algorithm is adopted to synchronously plan a composite path of a cableway, a track and a road, and a road network list map containing an obstacle avoidance topology is generated; recognizing a tower footing projection polygon in the three-dimensional scene, segmenting the types of surface attachments in combination with a digital orthoimage model image, and generating a surface feature list map with a water and soil conservation identifier; and establishing a mapping function of the terrain curvature and the environment water conservation rule, outputting equipment types and spatial poses according to the hoisting safety margin and the earth surface disturbance threshold value, and generating an equipment list map. According to the method, a unified space reference and multi-system dynamic cooperation mechanism is constructed, and the planning precision and construction compliance of three graphs and one table are remarkably improved.

The invention provides a flood simulation method driven by physical and data fusion, and belongs to the technical field of flood simulation, the method comprises the following steps: pre-training to obtain a qualified urban flood simulation model, a target loss function comprising a data supervision loss item and a physical constraint loss item, the physical constraint loss item is composed of a mass conservation residual error loss item, a momentum conservation residual error loss item and an energy conservation residual error loss item; inputting ground elevation data and historical meteorological data of a target area in a city into the hydrological model to obtain water depth grid data and flow velocity grid data, and inputting the water depth grid data and the flow velocity grid data into the city flood simulation model for prediction processing to obtain water depth grid data and flow velocity grid data of the target area at different time points in the future; and based on the data at different time points, generating an urban flood ponding dynamic evolution graph of the target area. The invention aims to improve the efficiency and accuracy of urban flood scene prediction.

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.

The invention relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on the digital twinning technology, and the method comprises the steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, obtaining the reflection intensity and elevation data of each point cloud, clustering all point clouds corresponding to the ground, and obtaining a three-dimensional model; recording the areas where all point clouds corresponding to the ground in each cluster are located as local areas; and calculating a coplanar characterization value, an erosion evaluation value, an instability risk value, a soil fixation evaluation value and a terrain evolution factor of each local area, and constructing a digital twinborn simulation platform of the river basin terrain in combination with the three-dimensional terrain model to perform analog simulation on flood disasters. The defect that a static terrain model cannot respond to terrain evolution caused by flood disasters can be avoided, simulation errors generated by a digital twin simulation platform in the flood disaster evolution process are effectively reduced, and the accuracy of risk assessment of the flood disasters is improved.

The invention relates to the technical field of agricultural data analysis, in particular to a land reclamation suitability evaluation and cultivated land quality improvement method based on big data, which comprises the following steps of: acquiring and superposing land utilization, soil and elevation data to construct a multi-source land attribute evaluation data set, calculating the distance from a pattern spot to a water source, reconstructing a weight through Gaussian attenuation, and calculating the land utilization, soil and elevation data; the method comprises the following steps: forming a spatial distance weighted feature matrix, extracting a gradient and an irrigation guarantee rate, performing weight reduction on an overrun part of a conflict threshold value, performing linear weighting calculation on a comprehensive score after normalization, dividing a regulation priority area, matching quality improvement measures, and forming a cultivated land quality improvement project layout map. A pattern spot level evaluation basis is formed through multi-source attribute space superposition, water source distance attenuation calculation is introduced to enable weight to change along with reachability, irrigation difference response is enhanced, conflict constraint correction is carried out on gradient and irrigation guarantee, adverse combination influence is inhibited, stable sorting is formed through uniform scale weighting, and the method is applied to the field of irrigation. And improvement priority area identification and quality improvement measure directional configuration are supported.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention relates to the technical field of automatic control, in particular to a depth control system and method for a seeder in a hilly and mountainous area, and the system comprises a visual module which is configured to collect a visual image of a to-be-sown area according to a first preset frequency; a data processing module configured to: convert the visual image into a depth map using an image processing model; performing gridding processing on the visual image; for each column of grids, determining at least one piece of target surface elevation data corresponding to the grid based on the depth map; determining at least one first adjustment instruction based on the preset seeding depth and the at least one piece of target surface elevation data; and the control instruction output module is configured to issue at least one first adjusting instruction corresponding to each column of grids to the corresponding depth limiting adjusting mechanism. According to the system, independent and accurate control over the ditching depth of each ditcher can be achieved through the visual preview and intelligent control technology, so that the seedling emergence uniformity and the crop yield are improved.

The invention discloses a culture pond monitoring method based on Otsuu-MNDWI collaborative screening and machine learning, and the method is characterized in that the method comprises the following steps: S1, collecting multi-source remote sensing data in a to-be-detected region; s2, carrying out image preprocessing on the surface reflectance data; s3, MNDWI calculation is carried out on the preprocessed surface reflectance data, and a corresponding MNDWI index map is obtained; s4, dividing the MNDWI index map into a water body region and a non-water body region by adopting an Otsu threshold segmentation method; s5, performing water body boundary extraction on the water body area obtained in the step S4 through a Canny algorithm; s6, taking radar VH polarization data, digital elevation data and global surface water body frequency data as auxiliary features, and performing multi-source data fusion and filtering on the auxiliary features and the water body region features obtained in the step S5; and S7, extracting the multi-dimensional feature of each pixel in the water body region processed in the step S6 as an input feature, inputting the input feature into the integrated learning model and training, and outputting a spatial distribution result of the culture pond.

The invention discloses an emergency rescue priority dynamic decision-making method and system, and the method comprises the steps: carrying out the grid division of a disaster region based on the ground surface attachment distribution in the disaster region and DEM digital elevation data, and carrying out the importance weight calculation, dynamic correction and visual coloring of a grid; in combination with a hydrodynamic model, predicting a substance motion trail after a geological disaster occurs, dynamically adjusting a potential influence range of each grid, updating a weight value, and performing secondary grid coloring; calculating a comprehensive signal characteristic value of the multi-source life signal data by adopting a multi-source signal fusion model, performing weight gain processing on a response grid, and performing weight return-to-zero and color reset on a non-response grid; generating a rescue priority sequence through a multi-dimensional decision tree model; the rescue priority and the resource allocation strategy are dynamically adjusted according to the real-time monitoring feedback data; and a Bayesian optimization model is established according to the rescue efficiency index data, and continuous optimization of a rescue strategy is realized through multiple iterations, so that the rescue efficiency is improved.

The invention provides a training method and application of a mountain torrent dangerous position prediction model. The training method comprises the following steps: acquiring digital elevation data, geological data, hydrological observation data, historical mountain torrent data and target rainfall intensity of a plurality of sub-basins; for any sub-drainage basin, according to the obtained data, simulating a submerged area and submerged water depth of the sub-drainage basin where the mountain torrent disaster occurs under the target rainfall intensity by using a hydrological hydrodynamic model, classifying the danger of the submerged area according to the submerged water depth, and setting a classification label; forming a plurality of mountain torrent inundation samples according to the inundation areas and the classification labels of the plurality of sub-basins, each mountain torrent inundation sample comprising three-band input data, the inundation area and the classification label of one sub-basin, and the three-band input data comprising digital elevation data, flow accumulation and gradient of the sub-basin; and training the initial mountain torrent dangerous position prediction model adopting the UNet network structure by taking the plurality of mountain torrent flooding samples as training data.

The invention discloses a geographic information survey data management system and method applying a GIS technology, and relates to the field of geographic information survey management. Multi-source information data collected by geographic information survey equipment is preprocessed to obtain a time sequence data structure and a transition data structure which can be spliced; acquiring elevation data at the splicing position of the target time sequence data structure and the adjacent time sequence data structure, calculating and adjusting the relative position relation between the target time sequence data structure and the adjacent time sequence data structure by using the splicing transition model according to an elevation data difference; determining the width of a transition region based on the adjusted elevation data difference at the splicing position, and smoothing the transition region through the splicing transition model; splicing the transition region after smoothing processing with the time sequence data structure, and completing smooth transition at the splicing position of the target time sequence data structure and the adjacent time sequence data structure; through the transition data structure and the processing logic thereof, the accuracy of the spliced digital terrain three-dimensional model is ensured to the greatest extent.

The invention relates to the technical field of airport runway detection and maintenance, in particular to a runway long wave unevenness resonance fragmentation fragmentation particle size measurement method, which comprises the following steps: carrying out unevenness evaluation based on airport runway three-dimensional elevation data, and collecting a fragmentation gray level image after airport runway resonance fragmentation; edge information is obtained, suspected edge discontinuous points are obtained through edge end points and edge trend directions, gradient reconstruction processing is carried out on the broken grayscale image in combination with a grayscale boundary balance value and a gradient adjustment coefficient, and a gradient reconstruction image is obtained; accurate recognition and enhancement of edge discontinuous points are realized, the recognition capability of the image on discontinuous edges is improved, and meanwhile, the situation that internal textures are misjudged as aggregate particle boundaries is avoided; and aggregate particles are segmented by using a watershed algorithm based on a control marker, and the crushing particle size is measured, so that comprehensive identification and quantitative analysis of the runway crushing particle size are realized, and common-frequency crushing construction is guided.

The invention discloses a hydro-junction ecological flow regulation and control system based on digital twinning, and relates to the technical field of intelligent ecological regulation and control. The system obtains digital elevation data DEM, geometric contour images and coupling power data of a riverbed and a wetland through multi-beam sonar, unmanned aerial vehicle remote sensing and a sensor group; and synchronization and preprocessing are realized in the twinborn simulation model to form a flow evolution data set and a water-sediment power data set. And constructing a dynamic connectivity driving force index Dcl based on the flow evolution data set to carry out flow driving force evaluation, constructing a comprehensive connectivity judgment index Eco to carry out ecological connectivity evaluation under the condition of sufficient driving force, and executing a corresponding regulation and control instruction according to an evaluation result. And dynamic adjustment of hub ex-warehouse flow and long-term maintenance of wetland ecological connectivity are realized. According to the system, real-time monitoring, evaluation and regulation of digital twin driving can be realized under a complex water and sediment power condition, and the guarantee capability of a hydro-junction to a wetland ecosystem is improved.

The invention relates to the technical field of fire-fighting navigation systems, and discloses a high-precision Beidou navigation system based on fire-fighting water source positioning. A water source feature acquisition module of the system acquires fire hydrant and underground pipe network surface material reflectivity data through a multispectral imager, and generates a water source feature vector set; the Beidou signal enhancement module is used for carrying out error correction on the original Beidou satellite signal by adopting a carrier phase difference technology; the dynamic base station network is used for deploying a plurality of base station nodes in an urban building dense area, and each node synchronously receives Beidou signals, calculates atmospheric delay errors and transmits the atmospheric delay errors to the central processing module through a wireless ad hoc network; the three-dimensional terrain matching module is used for carrying out iterative alignment on the building contour data scanned by the laser radar and the urban GIS elevation data, and establishing a centimeter-level precision three-dimensional model of the surrounding environment of the fire-fighting water source; and the multi-source data fusion module is used for receiving all the data and outputting optimal positioning coordinates of the fire-fighting water source.

The invention belongs to the technical field of image processing, and particularly relates to a mountain comprehensive ecological condition monitoring system based on remote sensing intelligent interpretation. The system comprises a remote sensing data processing unit, an object-oriented interpretation unit, an ecological patch diagram construction unit, a connectivity evaluation unit and an ecological output unit. The remote sensing data processing unit obtains a target mountain region remote sensing image and digital elevation data and generates a fusion base map; an object-oriented interpretation unit performs object-oriented segmentation on the fusion base map to generate an ecological patch object set; the ecological plaque graph construction unit constructs an ecological plaque graph through the ecological plaque object set; the connectivity evaluation unit generates an object state record and generates a connectivity anchor point object set; and the ecological output unit generates a comprehensive ecological condition score based on the connectivity enhanced ecological plaque object set and generates a comprehensive ecological condition assessment report. The spatial relation between the hydrological process and the ecological plaque can be stably expressed under the complex mountain terrain condition, and the influence of the initial interpretation error on the result is reduced.

The invention provides a rapid generation method and device for a typical vegetation pattern of a watershed, electronic equipment and a storage medium, and belongs to the technical field of data processing.The method comprises the steps that the resolution of a digital elevation model is determined based on the area of a target watershed; acquiring digital elevation data of the target drainage basin based on the resolution; based on the vegetation distribution morphological characteristics of the target watershed, determining a topographic index which is used for performing topographic unit demarcation of the target watershed; and based on the topographic indexes and the digital elevation data, differentially configuring the same kind of vegetation to different topographic units to obtain a typical vegetation pattern of the target watershed. According to distribution morphological characteristics of the basin vegetation pattern to be designed, proper topographic indexes are selected to delimit the basin topographic units, and the same number of similar vegetation is distributed in different topographic units in a differentiated manner to form a plurality of vegetation-topographic superposed patterns, so that rapid generation of grid data of the typical basin vegetation pattern is realized.

The invention discloses a method for generating a route of an autonomous inspection channel of an unmanned aerial vehicle, and the method is characterized in that the method comprises the steps: S1, channel selection and vectorization; s2, elevation processing mode decision making: calculating an elevation standard deviation in a frame selection area according to the imported DEM elevation data; dynamically adjusting the terrain clearance of the unmanned aerial vehicle according to the calculated elevation standard deviation; s3, automatic route planning comprises the steps of generating an initial waypoint, self-adaptive waypoint density and multi-angle coverage in a channel vector range; and S4, generating and verifying an air route file, wherein the air route file comprises standardized packaging and three-dimensional visual verification. An unmanned aerial vehicle inspection channel route is quickly generated through a two-dimensional plane map, and efficient and safe autonomous flight is realized by combining intelligent elevation processing and route optimization.

The invention discloses a three-dimensional geologic model construction method, a three-dimensional geologic model construction device, three-dimensional geologic model construction equipment and a computer readable storage medium, and the method comprises the steps: carrying out the elevation data fusion processing of collected high-resolution topographic data, and obtaining a digital elevation model; wherein the high-resolution topographic data comprises contour line data, remote sensing image data and field actual measurement elevation point data; performing interpolation processing on the digital elevation model based on an irregular triangulation network interpolation algorithm of geological constraints and interpolation weight coefficients in different directions to obtain stratigraphic interface data; wherein the geological constraints comprise sedimentary facies constraints, structural constraints and trend constraints; performing three-dimensional sealing processing on the stratum interface data based on a boundary representation method and the constructed lithologic attribute database to obtain a three-dimensional entity model; wherein the lithologic attribute database is constructed based on the spatial position information of the drill hole. The modeling precision of a complex structure is improved.

The invention provides a cleaning robot path planning method and system for a photovoltaic panel. The method comprises the following steps: acquiring three-dimensional elevation data and temperature distribution data of a photovoltaic array and current and voltage data of a photovoltaic string; according to the three-dimensional elevation data and the temperature distribution data, identifying a height mutation region and a shadow boundary region on the surface of the photovoltaic panel; generating a current and voltage characteristic curve according to the current and voltage data, and positioning a hot spot region associated with the shadow boundary region according to an abnormal peak point in the current and voltage characteristic curve; based on the spatial coordinate information of the height mutation region, the shadow boundary region and the hot spot region, generating a cleaning path plan which is used for defining cleaning priorities of the path avoidance region and the hot spot region; according to the invention, accurate perception of the complex environment on the surface of the photovoltaic panel is realized, intelligent path planning considering obstacle avoidance and hot spot preferential cleaning is generated, and the cleaning efficiency and the operation stability of the photovoltaic system are improved.

The invention discloses a high-resolution mode topographic data construction method, and the principle of the method is as follows: collecting elevation data based on an SRTM-DEM, and carrying out the hole filling of the collected elevation data through employing a moving surface fitting interpolation technology. According to the high-resolution mode terrain data construction method, a small and medium-scale mountain system which is difficult to depict by traditional terrain data is depicted, and implementation / copying / reproduction of a more practical and finer mode underlying surface terrain in the mode is facilitated.

The invention discloses a hyperspectral image and laser radar data fusion classification method based on an improved attention mechanism. The method comprises the following steps: obtaining hyperspectral image data and laser radar elevation data to be classified; processing the hyperspectral image data through a spectral channel attention module to extract spectral features; processing the laser radar elevation data through an elevation space attention module to extract spatial features; inputting the spectral features and the spatial features into a double-feature fusion module for coupling to generate fusion features; image classification is completed based on the fusion features; according to the method, the image features of the hyperspectral image in the spectral dimension and the elevation information of the radar image in the spatial dimension can be fully extracted, and the representation capability of the model for the spatial features and the spectral features is enhanced.

The invention provides an unmanned aerial vehicle cluster fire point real-time detection and positioning method and system. The method comprises the following steps: acquiring a visible image and an infrared image synchronously shot by a single unmanned aerial vehicle, and recorded shooting time and unmanned aerial vehicle pose data; processing the visible image and the infrared image based on threshold segmentation and morphological filtering to obtain a moment image pair; inputting the moment image pair into the trained double-attention segmentation network, and outputting a fire semantic map; analyzing and processing the fire semantic map based on the connected domain, outputting candidate fire points, and obtaining initial geographic coordinates of the candidate fire points based on the pose data of the unmanned aerial vehicle; based on the shooting time, tracking the initial geographic coordinates of the candidate fire points in real time through Kalman filtering, and outputting a fire point tracking and positioning result of the single unmanned aerial vehicle at the to-be-positioned moment through dynamic compensation of elevation data; and fusing the fire point tracking and positioning results of all the unmanned aerial vehicles in the unmanned aerial vehicle cluster at the to-be-positioned moment to obtain a fire point fusion positioning result at the to-be-positioned moment.

The invention provides a coastal zone land coverage type classification method based on a random forest and a topology rule, and belongs to the technical field of coastal zone land coverage type classification. The method comprises the following steps: based on a GEE cloud platform, integrating a multispectral remote sensing image and elevation data, extracting spectral features and geographic features, and realizing pixel-level classification of random forest machine learning; generating a clustering unit through a simple non-iterative clustering method, and performing object-oriented special classification on the water body and the aquaculture pond in combination with spectral and morphological characteristics of the clustering unit; spatial filtering is introduced to remove salt and pepper noise, a coastal zone ground feature topology rule base is established to carry out spatial logic verification, and coastal zone land coverage classification with high precision and reasonable spatial layout is realized. According to the method, the multi-source remote sensing data and the multi-level classification strategy are fused, the problems of coastal zone ground feature spectrum confusion, boundary fuzziness and space logic inconformity are effectively solved, the classification precision and practicability are remarkably improved, and the method is suitable for land coverage monitoring and management of the complex coastal zone environment.

The invention relates to the technical field of continuous casting billet detection, and discloses a continuous casting billet surface image defect detection method based on image processing, and the method comprises the following steps: collecting a continuous casting billet surface multispectral image sequence, separating an oxide scale and a matrix metal area by means of spectral reflectivity difference, and building a surface material distribution map; constructing a dynamic threshold segmentation model, generating an adaptive segmentation interval according to gray value fluctuation characteristics, and extracting a contour boundary of a potential defect area; establishing a defect morphological feature library, and screening candidate defect areas through multi-scale matching and geometric constraint; performing spectrum verification, and comparing the similarity between the reflectivity mutation curve and a preset defect characteristic curve; generating a three-dimensional shape reconstruction instruction, and triggering a laser displacement sensor to acquire elevation data; and establishing a defect classification decision tree, inputting elevation and multispectral features, and outputting a final defect category according to a material type and deformation degree combination rule.