5069 results about "Landform" patented technology

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention discloses a high-precision topographic surveying and mapping system and method based on an unmanned aerial vehicle, and particularly relates to the technical field of unmanned aerial vehicle surveying and mapping, and the method comprises the following steps: carrying out the topographic modeling before a task, recognizing GNSS shielding, and dividing a navigation region; gNSS, INS and visual inertial navigation positioning information are fused in flight, the weight is dynamically adjusted, and historical trajectory constraint is combined; constructing a closed-loop detection mechanism to carry out error feedback and feedforward prediction; confidence is calculated according to image definition, IMU stability and time synchronization deviation, high confidence points are screened for DEM reconstruction, local heavy flight is triggered by low confidence points, and surveying and mapping precision and reliability are improved; according to the method, the autonomous navigation and positioning precision of the unmanned aerial vehicle in a complex environment is improved, the quality control of surveying and mapping data and the DEM reconstruction reliability are enhanced, the closed-loop self-correction and feed-forward error prediction capabilities are provided, and the stability, precision and intelligent level of the system are remarkably improved.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention relates to the technical field of wetland carbon evaluation, and discloses a wetland carbon flux evaluation and management system based on three-dimensional digital twinning. The system comprises a three-dimensional modeling module, a flux monitoring module, a model optimization module and a twin updating module. The three-dimensional modeling module integrates wetland geographic space coordinates, an ecological parameter set and elevation, vegetation coverage density and soil type layering information, constructs a three-dimensional terrain grid model, and generates a three-dimensional digital twinborn body with a geographic mark; the flux monitoring module is used for collecting data such as atmospheric temperature gradient based on real-time environmental parameters, extracting carbon flux change characteristics through time sequence analysis and outputting a dynamic distribution state value; the model optimization module uses an adaptive evolutionary algorithm to adjust the parameter matching degree, iteratively updates constraint conditions, and generates an optimization parameter set; and the twin updating module recognizes abnormal grid units according to the abnormal grid units, corrects attributes by combining measured data, calibrates a topological relation, and outputs an updated evaluation model.

The invention discloses a dangerous rock falling motion feature prediction method based on multi-source data fusion, and belongs to the technical field of engineering geology. Comprising the steps of collecting dangerous rock multi-source data to construct a basic information base; processing dangerous rock multi-source data based on a CNN algorithm, identifying a dangerous rock contour and calculating geometric features; the dangerous rock multi-source data is processed through multiple algorithms, and the terrain is reconstructed to generate a digital elevation and terrain model; analyzing a vibration signal frequency spectrum, quantifying dangerous rock multi-source data features and determining a fusion weight; fusing the multi-source data of the dangerous rock to construct a three-dimensional real scene model and construct a physical and mechanical model; establishing a historical database, selecting a machine learning algorithm to construct a data driving model, and adopting an optimization algorithm to predict a rockfall movement track and state; and determining a two-dimensional simulation key section, establishing a two-dimensional numerical model, and dynamically simulating and calculating rockfall movement and rock-soil body response. According to the scheme, the defects in the aspects of dangerous rock falling motion feature prediction and protection design in the prior art are overcome, and the prediction accuracy and efficiency are improved.

The invention relates to the technical field of oceanographic engineering surveying and mapping, and provides an oceanographic engineering surveying and mapping deep sea terrain reconstruction method, which comprises the steps of multi-modal data synchronous acquisition, frequency domain feature cross enhancement, dynamic convolution feature fusion, inverse projection terrain reconstruction and terrain credibility verification. According to the invention, an acousto-optic multi-mode fusion technology is adopted, and the advantages of acoustic penetrability and optical high resolution are combined, so that the complex terrain detail capturing capability is improved; the dynamic convolution network adaptively adjusts convolution kernel parameters through frequency domain energy, the multi-scale feature fusion effect is enhanced, and the accuracy of terrain texture expression is improved; according to the motion distortion compensation method, carrier attitude drift is corrected in real time through inertial navigation data, and the geometric fidelity of a reconstruction model is guaranteed. The method is suitable for a deep sea complex environment, and provides high-precision terrain reconstruction method support for ocean engineering surveying and mapping and ocean resource exploration.

The invention relates to the technical field of meteorological data processing and geographic information, in particular to a near-surface meteorological field downscaling method based on a topographic constraint Transform model, which comprises the following steps of: extracting multi-modal fusion features from reanalysis meteorological data and digital elevation model data; mapping to a target resolution by means of a topographic constraint Transform network to obtain initial downscaling meteorological data; latent variable modeling is carried out on the initial data through a variational auto-encoder to describe multi-scale uncertainty, physical constraints such as energy balance and mass conservation are incorporated in combination with an iterative updating mechanism based on meta-learning, and core downscaling meteorological data are generated; according to the invention, detail capture and dynamic adaptation of local meteorology are realized through self-supervised pre-training and online updating.

The invention provides a GNSS-RTK-based topographic mapping method and system, and the method comprises the steps: obtaining multi-source positioning signal data in a target region, the multi-source positioning signal data comprising a satellite original observation value, a receiver antenna phase center deviation parameter, and real-time dynamic differential correction information; performing multi-path interference suppression processing on the multi-source positioning signal data to generate a phase observation sequence after interference suppression; inputting the phase observation sequence after interference suppression into a topographic feature calculation model, and extracting topographic elevation change feature parameters of the target area; performing spatial fusion processing based on the terrain elevation change characteristic parameters and reference elevation parameters in a preset terrain database to generate three-dimensional terrain curved surface data of the target area; and dynamically adjusting the topographic map updating frequency according to the difference degree between the three-dimensional topographic surface data and the historical topographic surveying and mapping result, and outputting a real-time topographic surveying and mapping result. According to the invention, precision, real-time performance and resource efficiency can be considered in complex terrain environment application.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention belongs to the technical field of topographic mapping, and discloses a topographic mapping system and method based on an unmanned aerial vehicle, and the method comprises the steps: a data collection module collects map geological data; the route planning module plans an optimal driving route; the data acquisition module acquires topographic images and point cloud data; the environment parameter monitoring module monitors flight environment parameters in real time; the image data analysis module is used for preprocessing and analyzing the image data; the radar data analysis module preprocesses and analyzes the radar data; the feature extraction and matching module performs feature extraction and matching on the image and the point cloud data; the terrain model construction module constructs a terrain three-dimensional model; and the terrain analysis module performs quantitative analysis on the terrain. According to the method, key feature extraction is performed on images and point cloud data, and image splicing and point cloud registration are realized; the terrain model construction module constructs a three-dimensional terrain model according to feature extraction and matching results, and the terrain analysis module obtains accurate terrain information.

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

Disclosed in the present invention is a bridge scour curved surface morphological feature reconstruction method based on three-dimensional sonar point clouds, comprising the following steps: collecting point cloud data of a three-dimensional morphology of an underwater riverbed terrain near a bridge foundation, and acquiring original point cloud data of a complete morphology of an underwater riverbed; performing denoising and ball pivoting reconstruction preprocessing on the collected original point cloud data to complete preliminary reconstruction of point clouds; on the basis of the point cloud data having undergone preliminary reconstruction, performing point cloud binarization-based scour morphology recognition on the basis of a computer vision principle, calculating scour pit element classification values of all the point clouds, and recognizing scour pit point clouds; on the basis of the recognized scour pit point clouds, using a K-means clustering algorithm for clustering, and calculating the maximum depth of each pier scour pit; and on the basis of the obtained scour pit element classification values of all the point clouds, providing a curved surface reconstruction method based on scour recognition to perform high-precision curved surface reconstruction on the scour pits. The present invention can achieve high-precision scour pit morphology reconstruction.

The invention relates to a terrain real scene modeling method, device and equipment and a storage medium thereof. According to the method, multi-source heterogeneous data related to the terrain in a target area is integrated, a multi-modal data cube is generated through noise filtering and data fusion under a unified coordinate system, then a data cavity area is identified based on density clustering and elevation gradient analysis, and geophysical features are extracted. Innovatively constructing a neural implicit inference model fused with physical constraints to perform joint modeling on the earth surface and underground structures, and finally converting implicit terrain representation into an explicit three-dimensional terrain model; seamless fusion modeling of the earth surface and the underground space under the complex shielding environment is achieved, the physical rationality of the model and the hidden area reconstruction precision are remarkably improved, meanwhile, it is ensured that the generated model strictly conforms to the geological physical law, and the geological engineering safety and the planning reliability are effectively guaranteed.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention relates to the technical field of risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention provides an electric power engineering survey data processing method and a related device, and the method comprises the steps: obtaining a multi-source survey data set of a target region, the multi-source survey data set comprises geological topographic data, electromagnetic interference data and equipment layout data, carrying out the joint feature mapping of the multi-source survey data set through a pre-trained feature extraction network, and obtaining a feature mapping result; a multi-dimensional survey feature vector is generated, the multi-dimensional survey feature vector is input into an optimization decision model for iterative parameter adjustment, an electric power engineering optimization parameter set of the target area is generated, and the optimization decision model is obtained through training based on a mapping relation between historical survey data and a verification optimization result; and generating an electric power facility deployment scheme of the target area according to the electric power engineering optimization parameter set, wherein the deployment scheme comprises a power transmission line path planning result, substation site selection coordinates and an electromagnetic interference suppression strategy. According to the method, the environmental adaptability, the global coordination and the construction performability of a power facility deployment scheme can be improved.

The invention discloses an intelligent geographic surveying and mapping data processing analysis method and system, and the method comprises the steps: obtaining satellite image data, LiDAR point cloud data and unmanned aerial vehicle aerial photography data, obtaining multi-source heterogeneous surveying and mapping data, building a DC-CNN dual-channel convolutional neural network model, adding a cross-channel feature fusion module on the basis of a model spectrum channel and a geometric channel, and carrying out the multi-source heterogeneous surveying and mapping data fusion. And utilizing a convolutional layer in a depth separable convolution substitution model, inputting the fused multi-source heterogeneous surveying and mapping data into the target DC-CNN network model for identification, and performing association modeling on the surveying and mapping feature data, topographic parameters and environmental data to obtain a multi-scale geographic knowledge map. The extraction efficiency of the model on key features is improved, so that the model has better real-time performance and expandability while keeping high precision.

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

The invention discloses a lake terrain prediction method based on machine learning fused multi-source satellite data, and relates to the technical field of machine learning, and the method comprises the steps: extracting the optical features of a target lake from an optical satellite image; extracting water features of each water body pixel in the target lake from the SWOT satellite data; performing space-time alignment and fusion on the optical features and the water features to obtain a fusion feature vector; the actually measured water depth point of the real water depth is matched to the geographic position of the corresponding pixel, and then the real water depth and the fusion feature vector of the corresponding pixel form a training sample; selecting a machine learning model and initializing the structure or parameters of the machine learning model; training the initialized machine learning model by using the training sample; utilizing the trained machine learning model to predict the water depth value of the corresponding pixel according to the fusion characteristic vector of each water body pixel; and determining the terrain of the target lake according to each water depth value. According to the method, the terrain of a large-scale lake can be efficiently predicted at low cost.