56 results about "Geographical feature" patented technology

A method and apparatus for assessing the hazard of rainfall-induced landslide clusters, and a computer device. The method comprises: acquiring historical rainfall-induced landslide cluster data from past intense rainfall events to construct landslide sample data; performing analysis on the basis of landslide attribute parameters and rainfall-induced landslide cluster data distribution information of the landslide sample data to obtain landslide development characteristics; on the basis of landslide geographical features and geometric features, using the landslide sample data and a plurality of candidate landslide-influencing indexes to analyze underlying patterns of landslide occurrence, so as to establish a landslide hazard evaluation index system; and performing correlation analysis on the plurality of candidate landslide-influencing indexes to determine a target landslide-influencing index, and combining the target landslide-influencing index with the landslide hazard evaluation index system to obtain a landslide hazard assessment model constructed on the basis of an automated machine learning framework. The efficiency of rainfall-induced landslide cluster hazard assessment can be improved and the accuracy of rainfall-induced landslide cluster hazard assessment can be effectively enhanced.

The invention discloses a wind power plant power prediction method and device based on space-time collaboration and a storage medium, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining historical wind field data and geographic space features of a wind power plant, the wind field data comprising fan operation data and meteorological feature data in the same time period; combining the historical wind field data, the geographic features and the corresponding total power labels of the wind power plant into a sample set, and training a wind power plant power prediction model by using the sample set; wherein the wind power plant power prediction model extracts local and overall double-layer similarity based on historical wind power plant data, obtains a time period cluster with high similarity with meteorological feature data from historical fan operation data according to the double-layer similarity, and performs spatial clustering on the time period cluster based on historical geographic features; and inputting the to-be-measured wind field data and the to-be-measured geographic features into the trained wind power plant power prediction model to obtain the total power prediction value of the wind power plant, thereby improving the wind power prediction precision and efficiency of the complex terrain.

The invention relates to an urban scale remote sensing image building contour extraction method considering geographic features, and the method comprises the steps: obtaining remote sensing image data, optical image data, land utilization data and night light data of a target region, and carrying out the preprocessing; dividing the target area into a plurality of grid units, classifying a building dense area and a building sparse area according to land utilization data, and calculating a noctilucent enhanced built-up area index of each grid unit; constructing a training sample set; the method comprises the following steps: constructing a double-branch encoder comprising a convolutional neural network branch and a Transform branch; a convolution interaction fusion module is embedded in each stage of the encoder, the training sample set is used for training to obtain a building contour extraction model, and the building contour extraction model is applied to contour extraction of urban scale images. The method has the beneficial effects that a new spatial sampling strategy is constructed, so that the training and deployment efficiency of the cross-large geographic region deep learning model is improved, and the data annotation cost is reduced.

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 provides a tunnel-slope system state information association mutual feedback model construction method and system, and relates to the technical field of geotechnical engineering monitoring, and the method comprises the steps: constructing a unified database through obtained target monitoring data, and analyzing an influence coefficient of a traffic load on slope stability in combination with a dynamic disturbance factor; on the basis of the coefficient, the geographic features and the stress data, completing unknown region data to form a complete data set; extracting a sensitive feature set, identifying a construction blasting and rainstorm rainfall interference section, and locking a side slope potential sliding surface and tunnel lining deformation collaborative feature interval; constructing a multi-index evaluation matrix, performing normalization and threshold dynamic adjustment, and screening an early warning index set; a dynamic model is established, and a tunnel-slope system state information association mutual feedback model is formed through a bidirectional feedback mechanism optimization model of slope displacement and tunnel excavation data. According to the invention, the accuracy and real-time regulation and control capability of tunnel-slope system state early warning are improved.

A method for discovering geofeatures in a subterranean formation includes receiving first input data including training seismic surveys. The method also includes training a model based on the training seismic surveys to produce a pre-trained model using a self-supervised learning (SSL) technique. The method further includes receiving second input data. The second input data includes one or more target seismic surveys associated with the subterranean formation. The method also includes generating geological representations of the subterranean formation using the pre-trained model and based on the one or more target seismic surveys. The method also includes generating a database based on the geological representations. The method also includes generating an output based on the database.

The invention discloses a risk management and control zoning method and system based on multi-medium interactive pollution data, and relates to pollution management and control, and the method comprises the steps: delimiting an initial risk pollution region through random pollution point detection, and determining main pollutants of each medium and the content of the main pollutants; analyzing the content change gradient of the pollution point, and constructing a content reduction vector line; setting a multidirectional edge region, analyzing vector line difference, and if the criterion does not reach the standard, expanding the edge until the criterion is met or crossed; accurately measuring an actual pollution range, extracting geographic features, and establishing a correlation group of pollutants, vector distribution and the geographic features; according to the method, vector distribution, geographic features and pollutants are taken as elements, a multi-medium interactive pollution comparison model is constructed, and real-time data are analyzed, so that the accuracy, dynamic adaptability and real-time response efficiency of multi-medium pollution risk management and control are improved, and efficient partitioning and early warning in a complex terrain scene are supported.

The invention discloses a grassland environment monitoring method and system, a storage medium and electronic equipment, relates to the technical field of grassland ecological monitoring, and aims to provide a monitoring method which has the advantages of dynamically adjusting sampling frequency and statistical period to balance monitoring precision and resource consumption, reducing data redundancy and missing, improving monitoring response timeliness and the like. The monitoring method comprises the following steps: determining a grassland type and a monitoring index according to climate conditions and geographic features of a monitoring target area, and setting an initial sampling frequency and an initial statistical period; based on the initial sampling frequency and the initial statistical period, a preset sensor is used for carrying out data collection on the monitored target area, and the collected data are stored; analyzing the collected data to obtain an analysis result; and based on the analysis result, the initial sampling frequency and the initial statistical period are dynamically adjusted to ensure the optimal balance between the monitoring precision and the resources.

This invention proposes a two-stage super-resolution reconstruction method and system for three-dimensional downscaled wind fields in bridge sites with complex terrain. First, the invention utilizes a WRF model and explicitly enables large eddy simulation to construct a high-fidelity training set. To build a spatially three-dimensional reconstruction result, the invention extracts dynamic meteorological sequences and static geographical features for different feature height layers. After filtering out invalid regions using a mountain occlusion mask matrix, three parallel cascaded reconstruction models are independently trained to significantly improve the real-time performance of data acquisition. The model training innovatively integrates terrain-forced physical loss and frequency domain energy constraints. Finally, the two-dimensional reconstruction results from each height layer are combined in vertical space to construct a high-precision three-dimensional microscale meteorological field ensemble, providing reliable technical support for three-dimensional wind energy resource assessment and bridge health monitoring in complex terrain.

The application provides a high-precision map regionalization updating method and system based on space-time correlation, and relates to the technical field of high-precision maps. The application binds real scene image data and associated data of a shooting terminal to form an image data unit with a space-time position marker. The image data unit is compared with a pre-stored real scene three-dimensional map to determine satellite positioning information correction coordinates, identify geographical feature characteristics, and distribute the geographical feature characteristics to corresponding space-time grid units according to the correction coordinates. Spatial clustering analysis is performed according to the distribution of the space-time grid units to determine key areas that need to be traced back. Image data units and geographical feature characteristics at different historical times are obtained through time retrieval and spatial indexing to reconstruct a time sequence change state. According to the difference between the time sequence change state and the pre-stored map, the real scene three-dimensional map is updated in a regionalized attribute, which can realize regionalized attribute updating of a high-precision map and improve the accuracy and pertinence of map updating.

According to the wireless channel error distribution modeling method based on empirical formula and conditional generative adversarial network fusion, topographic features, physical error constraints and a conditional generative adversarial mechanism are introduced, environmental features such as terrains and buildings are used as conditional input of a conditional generative adversarial network (cGAN), adversarial training is carried out in combination with real propagation error data, and the accuracy of confrontation is improved. The model can learn a nonlinear mapping relation between geographic features and propagation errors, and a channel modeling result with physical consistency and terrain correlation is obtained, so that a traditional path loss model or a deep learning prediction model can be effectively assisted to be optimized; and reliable data support is provided for wireless propagation modeling and related model research in a scene with data scarcity or complex environment.

This system provides an optimization method that can expand service area coverage, which is affected by terrain-related interference such as radio wave blockage and diffraction losses, while also reducing the computational load required for that optimization. [Solution] For each of the multiple candidate locations in the sky where multiple aerial PFs can be placed, the system acquires data on the radio wave propagation characteristics of the radio wave propagation path between the candidate location and the service area, based on the geographical characteristics of the service area. Based on the radio wave propagation characteristics data, it determines the locations of the multiple aerial PFs that will form cells toward the service area, or both the number and locations of the multiple aerial PFs. Based on the radio wave propagation characteristics data, it determines the values ​​of the beam parameters to be used to form cells for the multiple aerial PFs.

The application provides a tunnel-slope system state information correlation feedback model construction method and system, relates to the geotechnical engineering monitoring technical field, and wherein the method comprises: constructing a unified database through the obtained target monitoring data, combining dynamic disturbance factor analysis, and analyzing the influence coefficient of traffic load on slope stability; based on the coefficient, the geographical features and the stress data, the unknown area data is completed to form a complete data set; the sensitive feature set is extracted therefrom, the construction blasting and the heavy rain interference section are identified, the potential sliding surface of the slope and the deformation coordination feature interval of the tunnel lining are locked; a multi-index evaluation matrix is constructed and normalized and threshold dynamic adjustment is performed, and the early warning index set is screened; a dynamic model is established, the model is optimized through the two-way feedback mechanism of the slope displacement and the tunnel excavation data, and a tunnel-slope system state information correlation feedback model is formed. The application improves the accuracy and real-time control ability of the tunnel-slope system state early warning.

The invention provides an ecological protection red line management system, and relates to the technical field of ecological management. The ecological protection red line management system comprises a system data arrangement and basic analysis module, a data acquisition processing and library building module, a data publishing module, a geographic information system development and construction module and an ecological protection red line protection system. And the system data arrangement and basic analysis module is used for analyzing and arranging spatial distribution geographic features and basic data information of the ecological protection red lines in the management area. By designing the comprehensive management system, the management requirement of the environmental protection department for the ecological protection red line, the maintenance requirement of the management and protection personnel for the ecological protection red line management system and the query and consultation requirements of related personnel for the ecological protection red line are met, and network sharing and data maintenance of ecological protection red line data are realized; and a basic platform is provided for servitization and business of ecological protection, environment management, business query, information release and the like of the ecological protection red line.

The application relates to a three-dimensional sea temperature monitoring method, which is based on low-resolution sea temperature background data, meteorological data and geographical feature data of a sea area to be monitored, predicts residual errors through a sea surface temperature reconstruction model, superimposes the residual errors on the low-resolution sea temperature background data, obtains high-resolution sea temperature parameters of each region of the sea surface, and improves the accuracy and physical consistency of the sea surface temperature parameters. In deep sea temperature inversion, based on biochemical environmental data and high-resolution surface sea temperature parameters, the sea temperature parameters of each depth region of the sea area to be monitored are predicted through a deep sea temperature prediction model, and the heat exchange law is more accurately reflected. The surface reconstruction and the deep inversion are combined, a connection mechanism is established, information is collaboratively integrated, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for marine heat transport calculation and other applications.

This invention discloses a method for constructing a distributed flood forecasting and scheduling model based on sub-basins, specifically relating to the field of flood forecasting model optimization technology. It addresses the problem of spatiotemporal heterogeneous local prediction failure in existing flood forecasting models due to neglecting parameter sensitivity. By dividing the target basin into sub-basins and extracting hydrogeographical feature parameters, a cross-basin hydraulic facility topology network is constructed to dynamically correct river evolution boundary conditions. The spatial heterogeneity of parameter sensitivity is quantified using the Sobol index method, and sensitive anomaly response zones are identified by combining historical flood event distributions. Spatial lag correction is applied through the river topology network to generate parameter sensitivity level classifications. A dynamic weight matrix is ​​constructed, and regional parameter calibration is performed using both global optimization and local adjustment strategies. The hydraulic balance constraints of the calibration results are verified based on river topology relationships, and a regionalized and graded flood discharge scheduling scheme is generated after iterative correction, significantly improving forecasting capabilities in sudden flood scenarios.

The invention discloses a meteorological early warning service platform based on urban feature data, and the platform comprises a region feature collection module which is used for collecting the community-level geographic feature data of a target region, and the data comprises the terrain elevation, the surrounding river distribution distance, the building construction duration, the drainage reconstruction mark, and the building height; the meteorological monitoring module is used for acquiring precipitation, wind speed and temperature data in real time; the regional feature analysis module is used for generating a vulnerability score reflecting the inherent risk level of the region by fusing the terrain elevation, the peripheral river distribution distance, the building construction duration, the drainage reconstruction mark and the building height; and the regional meteorological early warning module is used for generating a dynamic graded early warning signal by coupling the vulnerability score with the real-time precipitation, wind speed and temperature data. According to the invention, urban features and meteorological data can be fused, risks are dynamically quantified, accurate early warning is realized, and the urban meteorological disaster prevention efficiency is improved.

The invention discloses a drinking water source pollution monitoring and early warning method and system, and the method comprises the steps: obtaining the hydrological and geographic features of a drinking water source, and determining a plurality of monitoring points of the water source; generating a real condition according to the respective multi-source monitoring data of the plurality of monitoring points, and transmitting the multi-source monitoring data; performing preprocessing and spatial fusion processing on the multi-source monitoring data to obtain a water quality parameter observation value of the water source; correcting the water quality parameter observation value according to the dynamic baseline value of the water quality parameter observation value to obtain an improved water quality anomaly score; according to the improved water quality abnormity score, pollution identification and traceability analysis are carried out on the water source; and generating an early warning notification message according to a tracing analysis result. Through arrangement of multiple monitoring points and spatial fusion of multi-source monitoring data, the situations that monitoring data fluctuation is too large and overall monitoring is unstable due to a single monitoring point are avoided, and monitoring timeliness and early warning accuracy and credibility are improved.

The invention discloses a road loss prediction method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring map information and road test data of a target area; according to the map information, extracting a geographic feature vector of the map information through a geographic pre-training model based on comparative learning training; according to the road test data and the geographic feature vectors, road loss prediction is carried out through a pre-trained road loss prediction regression model, and a road loss prediction value of the target area is obtained.According to the road loss prediction method and device, the road loss prediction is carried out through the geographic pre-training model based on comparative learning training, the direction of model feature extraction can be guided, and the accuracy of road loss prediction is improved. And the pressure of model fitting is reduced, so that the migration scene prediction accuracy is improved.

The application discloses a short-term photovoltaic power prediction method suitable for multi-county geographical heterogeneity, and relates to the technical field of photovoltaic power prediction; the method comprises the following steps: a data preprocessing step, collecting multi-county meteorological and power generation data, performing time alignment, missing value filling, abnormal value elimination and feature engineering processing; a county heterogeneity quantification step, performing K-means++ clustering based on geographical features and meteorological statistical features, and quantifying the power contribution degree of each factor by adopting a SHAP value analysis method; an adaptive feature screening step, screening core features based on a factor weight vector, and generating a county exclusive feature subset; a mixed model integration step, constructing an LSTM, XGBoost and GPR basic model library, and integrating by dynamically distributing model weights by adopting an AdaBoost algorithm; and a summer and autumn scene optimization step, applying special correction to different county scenes. The application fully considers the influence of geographical heterogeneity on photovoltaic power generation, realizes accurate prediction of multi-county photovoltaic power, and improves the prediction accuracy.

The invention relates to the technical field of risk distribution calculation methods, and discloses a method for calculating ship collision risk distribution in a port channel water area, and the method comprises the following steps: 1, defining a channel ecological space unit and a ship ecological unit, the channel ecological space unit is used for delimiting and associating a fixed feature library based on port channel function partitions, geographic features and traffic flow, and the ship ecological unit comprises basic attribute dimensions, motion attribute dimensions and intention attribute dimensions; and step 2, sensing and complementing multi-dimensional collaborative data. Through adoption of an edge-cloud collaborative architecture and edge nodes, local risks are processed in real time, pre-judgment information is transmitted in a cross-node linkage manner, a cloud integrates global data to generate a thermodynamic diagram and a risk transmission path, and through layered calculation and non-full data interaction, local risk millisecond-level response is realized, global risks are ensured not to be missed, and the risk transmission efficiency is improved. And the port channel dynamic management and control requirements are met.

The present application relates to the technical field of base station coordinate calibration, and particularly relates to a near base station position association calibration method fusing user trajectory LBS data, which can specifically include obtaining user trajectory LBS data of a target base station and a near base station; based on the relative position relationship between the target base station and the near base station and the user trajectory LBS data, respectively determining an association degree index of the near base station relative to the target base station, geographical features of the near base station and the target base station, and a calibration contribution index of the near base station relative to the target base station, and then calculating to obtain a calibration weight of the near base station relative to the target base station, and calibrating the position information of the target base station according to the calibration weight. The technical scheme provided by the present application can obtain a near base station with higher calibration contribution to the target base station, avoid the deviation interference influence of complex terrain environments such as cities on the position calibration of the target base station, and improve the accuracy and stability of the position calibration of the target base station.

The application discloses a water body pollution detection method and system based on satellite thermal infrared remote sensing, and relates to the technical field of water body pollution detection. The method comprises the following steps: collecting thermal infrared remote sensing parameters, environmental monitoring parameters and geographical feature parameters, constructing a detection basic parameter set, and extracting an effective water body grid; combining the thermal infrared remote sensing parameters of the effective water body grid with the environmental monitoring parameters, dynamically optimizing temperature inversion model parameters, performing temperature inversion, and obtaining water body surface temperature; performing pollution-free grid screening based on the water body surface temperature, and obtaining water body thermal infrared reference parameters of the pollution-free grid; comparing the water body surface temperature with the water body thermal infrared reference parameters, identifying and extracting water body thermal anomaly grid parameters; and performing spatial feature analysis based on the water body thermal anomaly grid parameters, positioning a water body pollution discharge outlet, and obtaining a pollution diffusion range. The application effectively improves the precision of water body pollution detection and the accuracy of spatial positioning of the pollution discharge outlet.

The invention provides a regional ecosystem health and hydrothermal condition collaborative optimization method, and the method comprises the steps: evaluating the ecosystem health and hydrothermal conditions of a region respectively; evaluating the coupling coordination relationship between the ecological system health and the hydrothermal condition based on the coupling coordination degree quantified by the coupling coordination degree model; the driving rule of the coupling coordination relation is explored in combination with a geographic detector and multi-scale geographic weighted regression, and a differential improvement strategy of the coupling coordination degree is proposed for different geographic features in a region based on a driving analysis result so as to promote collaborative optimization of health and hydrothermal conditions of a regional ecological system. According to the method, collaborative optimization of regional ecological system health and hydrothermal conditions is promoted more reasonably, comprehensively and accurately, and scientific support is provided for regional ecological management and ecological civilization construction.

The invention relates to the technical field of field intensity prediction, in particular to a method and a system for performing field intensity prediction based on high-dimensional features and a gradient boosting model, and the method comprises the following steps: obtaining physical data of a radio propagation path, the physical data at least comprises transmitting end geographic feature data, receiving end geographic feature data, path topographic data, path ground object data and propagation signal feature data; determining obstacles from the ground objects passed by the radio propagation path, and determining a plurality of dominant obstacle points from the obstacles; calculating a multi-dimensional characteristic quantity according to the physical data, wherein the characteristic quantity is used for representing at least one physical characteristic of a radio propagation path: a propagation characteristic, a topographic characteristic, a signal intensity characteristic, a shielding characteristic and a diffraction characteristic; and training the multi-dimensional characteristic quantity by using a gradient lifting model based on a decision tree to obtain a trained field intensity prediction model, and predicting the field intensity of the target path. According to the invention, the efficiency and accuracy of wireless electric field intensity prediction can be improved.

The application provides a reverse geocoding method, device and electronic equipment. The method comprises: obtaining a target latitude and longitude coordinate; obtaining target space index data corresponding to the target latitude and longitude coordinate in preset space index data, wherein the preset space index data comprises space indexes of geographical description contours in hierarchical grids, the hierarchical grids comprise a plurality of space grids, and each space grid corresponds to the space index of a geographical feature contour in the geographical description contours; determining target geographical feature contour information corresponding to the target latitude and longitude coordinate according to the target space index data; and performing attribution detection on the target latitude and longitude coordinate according to the target geographical feature contour information to obtain reverse geocoding information of the target latitude and longitude coordinate, wherein the reverse geocoding information comprises address description information used for describing the contained relationship between the target latitude and longitude coordinate and a point of interest (POI). The embodiment of the application can improve the accuracy of obtaining position information through reverse geocoding.

The invention belongs to the field of urban resident traffic trip next position prediction, and discloses a traffic trip next position prediction method and device based on multi-adaptive hypergraph collaboration, and the method comprises the steps: constructing an interactive hypergraph and a directed hypergraph based on an individual traffic trip trajectory sequence, constructing a geographic hypergraph based on geographic feature data of a position, and constructing a traffic trip next position prediction result. The interaction hypergraph and the geographic hypergraph are updated to obtain a self-adaptive interaction hypergraph and a self-adaptive geographic hypergraph, and a position overall embedding matrix and an individual overall embedding matrix are obtained based on the hypergraphs; and outputting the first k positions with the highest probability as a prediction result of the next travel position of the individual through a normalization function. The method has high accuracy and strong robustness for position prediction.

The application discloses a complex-terrain-oriented unmanned aerial vehicle true-height flight path planning method, system, device and medium. The complex-terrain-oriented unmanned aerial vehicle true-height flight path planning method comprises the following steps: acquiring multi-source geographic data, wherein the multi-source geographic data comprises at least one of terrain elevation data, building top elevation data, no-fly area data, high-voltage line data and geographic feature data; performing target obstacle identification on the multi-source geographic data corresponding to a planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional grid conversion based on the target obstacle data and the geographic feature data to obtain converted multi-source geographic data, wherein the converted multi-source geographic data comprises obstacle grids, priority grids and ordinary grids; performing path search on the converted multi-source geographic data to obtain two-dimensional flight path data; and obtaining three-dimensional unmanned aerial vehicle true-height flight paths based on the two-dimensional flight path data, the terrain elevation data and the building top elevation data.

The invention discloses an unmanned aerial vehicle true height route planning method, system and device for complex terrains and a medium, and the method comprises the steps: obtaining multi-source geographic data, the multi-source geographic data comprises at least one of terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data and geographic feature data; performing target obstacle identification on the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional grid conversion based on the target obstacle data and the geographic feature data to obtain converted multi-source geographic data, the converted multi-source geographic data comprises an obstacle grid, a priority grid and a common grid; performing path search on the converted multi-source geographic data to obtain two-dimensional route data; and based on the two-dimensional route data, the terrain elevation data and the building top elevation data, obtaining a three-dimensional unmanned aerial vehicle true height route.

The present invention provides a device for identifying deteriorated geological features, etc., that can identify deteriorated geological features from among a large number of them. [Solution] A moving object receives reflected light from a geographical feature after emitting light, acquires reflected intensity data measured by the moving object, and identifies the deteriorated geographical feature based on the acquired reflected intensity data.