128 results about "Geographical feature" patented technology

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention discloses a land utilization dynamic change monitoring method and system based on multi-modal remote sensing data, and relates to the technical field of multi-modal remote sensing data analysis. The system comprises a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module and an abnormality diagnosis and processing module. The data acquisition and preprocessing module realizes space-time alignment of multi-source data; the consistency baseline construction module divides land utilization types based on geographic features and establishes a baseline database; the real-time data analysis module screens suspected abnormal areas through difference indexes; the abnormity diagnosis and processing module distinguishes data quality abnormity and emergency abnormity based on the characteristic association relation, the data quality abnormity triggers data restoration and verification, and the emergency abnormity marks a high-priority area and outputs position information. According to the system, through multi-modal data fusion and correlation analysis, high-precision monitoring and abnormal intelligent classification of land utilization changes are realized.

The invention provides a water source protected area pollution analysis method and system based on software model simulation, and the method comprises the steps: firstly obtaining a pollution source data set and a geographic feature data set of a target water source protected area, then calling a software model to carry out the dynamic pollution diffusion simulation processing of the pollution source data set, and obtaining a pollution diffusion feature set; and then carrying out spatial influence analysis on the pollution diffusion feature set based on the geographic feature data set, and generating a pollution influence analysis result containing a pollution migration path, an influence region boundary and a time distribution interval. And then determining a pollution protection priority set according to the result, generating a differentiated protection strategy set in combination with a preset emergency response rule, and finally feeding back the differentiated protection strategy set to the water source protection management system to trigger pollution protection operation, so that accurate and efficient pollution protection of the water source protection area can be realized.

The invention relates to a chemical plant dangerous area electronic fence generation method and system, and the method comprises the steps: obtaining the positioning data of a target person, the positioning data of production equipment, and the video monitoring data and environment sensing data of a target area, carrying out the recognition based on the video monitoring data, and obtaining the behavior data of the target person, the method comprises the steps of dividing a target area into virtual grids based on a dynamic grid algorithm, determining distribution of geographic features based on GIS geographic information of the target area, filtering impassable areas based on the distribution of the geographic features, aggregating and combining adjacent grids, generating a closed boundary, and establishing a risk level evaluation model to evaluate the risk level of each virtual grid. The boundary adjusting module is used for adjusting the minimum buffer area width of the closed boundary to obtain a final electronic fence boundary, and the electronic fence boundary is used for carrying out risk early warning. According to the invention, dynamic and accurate sensing and protection of the high-risk area in the chemical plant are realized.

The invention discloses a meteorological disaster early warning response method and system based on a meteorological early warning aircraft, and the system comprises a data collection module which is used for constructing a database containing the real-time meteorological data, the terrain data, the historical disaster data and the early warning aircraft operation data; the multi-disaster terrain adaptation analysis module is used for analyzing waterlogging and gale comprehensive risks by using a synchronous superposition model in plains and analyzing landslide and thunder-gale chain risks by using a time sequence chain model in mountainous areas through a double-branch fusion neural network; the early warning response strategy generation module is used for generating differentiation strategies according to regional risks and terrains; and the terminal interaction module pushes information and receives feedback. The system dynamically generates protection guidance for different geographic features such as a plain high-risk area and a mountainous area high-risk area through multi-source meteorological data integration and analysis, and improves the timeliness and coverage of early warning information.

The invention discloses a carbon emission fusion model modeling method and system based on multi-dimensional geographic data, and relates to the technical field of carbon emission analysis and management. Comprising the following steps: collecting multi-dimensional geographic feature data and carbon emission data, and preprocessing the data; constructing a multi-dimensional geographic data carbon emission correlation model; training and calibrating the model, and evaluating the performance of the model; fusion analysis of multi-dimensional geographic factors and carbon emission is carried out, and decision support is provided for regional low-carbon management. According to the method, the carbon emission data and the multi-dimensional geographic feature data are collected and preprocessed, so that standardization and space matching of the data are realized; by constructing a multi-model combination of a spatial autoregression model and a geographically weighted regression model, the spatial dependency relationship between regions and the regional heterogeneity of geographic features can be captured at the same time; and carrying out quantitative analysis on the importance of the multi-dimensional geographic features through a random forest model, and identifying key factors influencing carbon emission.

The invention relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographic weighting, and the model comprises a feature decoupling module which is used for constructing a two-channel input structure of geographic features and non-geographic features, and achieves the feature decoupling and fusion through a multi-scale spatial weight matrix; the bandwidth allocation module dynamically determines the bandwidth scale in the XGBoost tree splitting process, and establishes a dynamic weight bound by tree levels; the constraint gain module generates split points with scale marks; a contribution decoupling module extracts split features and associates geographic and non-geographic features to realize salinization prediction and contribution index extraction; and the verification optimization module optimizes model parameters through a multi-scale thermodynamic diagram and spatial autocorrelation analysis. The model can capture geospatial effects of different scales, improves the spatial local prediction precision, and is applied to soil salinization analysis scenes with spatial heterogeneity.

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 relates to a three-dimensional sea temperature monitoring method, and the method comprises the steps: predicting a residual error through a sea surface temperature reconstruction model based on low-resolution sea temperature background data, meteorological data and geographic feature data of a to-be-monitored sea area, superposing the residual error with the low-resolution sea temperature background data, and obtaining a high-resolution sea temperature parameter of each region of a sea surface; and the precision and the physical consistency of the sea surface temperature parameters are improved. In deep sea temperature inversion, based on biochemical environment data and high-resolution surface sea temperature parameters, sea temperature parameters of all depth areas of a sea area to be monitored are predicted through a deep sea temperature prediction model, and a heat exchange rule is reflected more accurately. According to the method, surface reconstruction and deep inversion are combined, an association connection mechanism is established, information collaborative integration is achieved, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for applications such as ocean heat transfer calculation.

The invention relates to a multi-granularity interest point recommendation method based on a geography-time mixed tree structure. The method comprises the following steps: collecting sign-in data of each user; clustering the geographic positions of the sign-in points to obtain a clustering result containing a plurality of geographic clusters, dividing each piece of sign-in data into each geographic cluster, and distributing corresponding geographic cluster labels; segmenting time, counting geographic clustering labels of all sign-in points in all time periods to obtain a track-level geographic hierarchical structure, and organizing the track-level geographic hierarchical structure into a tree structure to obtain a geographic tree and a time tree; a multi-level recurrent neural network is adopted to establish a recommendation model, geographic features and time features are obtained based on the geographic tree and the time tree, the input of the recommendation model is the geographic features and the time features, and the output of the recommendation model is a prediction result of interest point recommendation; inputting the spatio-temporal information of the current user into the trained recommendation model, and outputting the predicted next potential interest point by the recommendation model; and a more accurate interest point recommendation result is obtained.

The invention provides an engineering investigation surveying and mapping intelligent management method and system based on a geographic feature processing technology. The method comprises the following steps: acquiring building distribution density, ground gradient and underground rock stratum distribution data of a target area, deploying a ground deformation monitoring radar at a selected position based on the building density and the rock stratum data, generating ground deformation data through radar images, and then separating ground subsidence change and original terrain characteristics to form a subsidence map; meanwhile, image reflection characteristics are analyzed to mark dangerous areas. And then integrating the ground gradient, the settlement trend and the rock stratum structure complexity to construct a land bearing evaluation model. The influence weight is further adjusted in combination with engineering requirements, and a bearing force evaluation result is generated after the model is input. Therefore, a scientific three-dimensional development planning scheme is generated, and optimized utilization and risk management and control of land resources are realized. According to the invention, through multi-source data fusion and model calculation, full-process analysis from land deformation monitoring to load bearing evaluation is realized.

The invention provides a channel navigation condition comprehensive assessment method based on multi-technology fusion, and relates to the technical field of waterway transportation safety monitoring and risk assessment. The method comprises the following steps: acquiring bridge distance, illumination, vibration data and channel clear height information in real time, and acquiring geographic features in combination with satellite remote sensing images; constructing a navigation channel accident influence coefficient and ship navigation risk degree model, and identifying a high-risk area through improved K-means clustering; the game theory is adopted to optimize the weight, and the Mahalanobis distance is combined to realize risk positioning; constructing an evaluation matrix based on the remote sensing image to divide risk levels; a multi-threshold early warning mechanism is set, and information is pushed in real time through a wireless sensor network; and outputting channel planning, scheduling suggestions and safety prompts. The method breaks through the limitation of traditional static assessment, self-created parameters quantify accident influences and ship risks, an optimization algorithm improves weight objectivity and clustering precision, early warning response reaches a millisecond level, risk positioning errors are reduced by more than 40%, and intelligent support is provided for inland waterway safety management.

The invention discloses a mountain torrent disaster classification method, device and equipment, and relates to the technical field of disaster monitoring, and the method comprises the steps: obtaining a geographic feature data set of a plurality of drainage basin nodes of a target region, and constructing a feature matrix according to the geographic feature data set; inputting the feature matrix into a mountain torrent disaster classification model, and outputting the classification probability that each drainage basin node belongs to each mountain torrent disaster classification; the mountain torrent disaster classification model is used for calculating an edge weight matrix of a graph structure through a multi-head attention mechanism according to the feature matrix and the graph structure formed by drainage basin nodes of the target area; sparse processing is carried out on the edge weight matrix, and graph convolution operation is carried out based on the feature matrix and the edge weight matrix after sparse processing to obtain aggregation features; performing global feature enhancement on the aggregated features to obtain enhanced features; and outputting the classification probability that each drainage basin node belongs to each mountain torrent disaster classification according to the enhanced features. The accuracy of mountain torrent disaster classification can be improved.

The invention provides a high-production planting method and system for realizing agricultural multi-dimensional elements based on geographic information, and relates to the technical field of geographic information, and the method comprises the steps: S1, obtaining multi-source geographic information data of a target planting region, comprising soil type spatial distribution data, climate condition statistical data, landform three-dimensional feature data and pest and disease damage occurrence records; s2, carrying out spatial superposition and correlation analysis on the multi-source geographic information data, and generating a multi-dimensional geographic element comprehensive layer integrating the soil fertility grade, the moisture infiltration capacity and the accumulated temperature illumination adaptation degree; and S3, presetting two target detection points in the multi-dimensional geographic element comprehensive layer, wherein the detection points are respectively located at a geographic feature turning point and a region center point of the planting region boundary. By integrating multi-source geographic information, accurate division of planting areas, intelligent matching of varieties and parameters and dynamic regulation and control of growth cycles are realized.

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 belongs to the field of tourism planning, and provides a tourism service method and system integrating local culture and tourism characteristics, and the method comprises the steps: collecting local culture data; collecting tourism characteristic data; defining each cultural activity and scenic spot as a node in the graph; establishing a multi-dimensional edge connection system for the graph; based on the constructed graph structure, fusing the geographic features and the time features into a multi-scale high-dimensional spatial-temporal feature space; constructing a hierarchical reinforcement learning framework; introducing a multi-target reward function into the reinforcement learning framework, wherein the multi-target reward function comprises a culture experience integrity index, a space-time coordination index and a risk avoidance index; and receiving a culture type and a scenic spot type which the user wants to embody, and generating an optimal route and travel time which meet the requirements of the user through the hierarchical reinforcement learning framework in the spatial-temporal feature space. The method is especially suitable for tourist route planning with rich local culture and complex geographical environment in Sichuan and Tibet areas and the like.

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 vehicles, in particular to a full-scene driving condition development method and device fusing geographic features, and the method comprises the steps: obtaining the current operation data of a vehicle and the current geographic information data of the position where the vehicle is located; based on the current operation data and the current geographic information data, a first alternative working condition set meeting preset characteristic parameter deviation is determined from a preset road scene database, and the preset road scene database is obtained based on a dynamically adjusted multi-dimensional Markov model; the dynamically adjusted multi-dimensional Markov model is obtained by dividing state variables dynamically generated by historical operation data and historical geographic information data; and determining a target driving working condition of the vehicle from the first alternative working condition set based on the current operation data and the current geographic information data. Therefore, the problems of lack of geographic feature fusion, incomplete speed section coverage, low working condition generation accuracy and the like in related technologies are solved, and a scientific basis is provided for vehicle design and road planning.

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.

The invention relates to an electrical carbon display method and device based on visual coding and computer equipment. The method comprises the following steps: acquiring geographic element data and electric carbon data corresponding to a target geographic area; the electricity and carbon data serve as attribute information of the geographic element data and are embedded into the geographic element data, and fusion data are obtained; performing grading processing on attribute information in the fused data to obtain a grading result; determining a visual coding pattern corresponding to the fused data according to a grading result; and according to the visual coding style, loading the fusion data into a target geographic area of the map page, so as to display the electricity and carbon data at an associated position of the target geographic area of the map page. By adopting the method, the electrical carbon readability can be improved.

The invention provides a high-precision map regionalization updating method and system based on space-time relevance, and relates to the technical field of high-precision maps, and the method comprises the steps: obtaining live-action image data and associated data of a shooting terminal, and binding the live-action image data and the associated data to form an image data unit with a space-time position mark; comparing the data with a pre-stored live-action three-dimensional map, determining satellite positioning information correction coordinates, identifying geographic element features and distributing the geographic element features to corresponding space-time grid units according to the correction coordinates; performing spatial clustering analysis according to space-time grid unit distribution, determining a key area needing to be backtracked, obtaining image data units and geographic element features at different historical times through time retrieval and spatial indexing, and reconstructing a time sequence change state; according to the difference between the time sequence change state and the pre-stored map, regionalized attribute updating is carried out on the live-action three-dimensional map, regionalized attribute updating of the high-precision map can be achieved, and the accuracy and pertinence of map updating 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 application discloses a power transmission line fire alarm method and system based on multi-source data fusion, the method comprising: monitoring fire points according to ground monitoring device monitoring data, unmanned aerial vehicle monitoring data and satellite monitoring data of a target area where the power transmission line is located; when it is judged that there is a fire point according to the satellite monitoring data and multiple satellites monitor the fire point, fusing the fire point information and monitoring data of the multiple satellites, generating time series data corresponding to the satellites, then acquiring geographical data and meteorological data of the target area, and respectively extracting geographical features corresponding to the geographical data, time series data and time series features corresponding to the meteorological data, splicing the geographical features and the time series features, obtaining a feature vector, identifying the fire point through a preset classification algorithm, judging whether the fire point monitored by the satellite is a real fire point, and if so, alarming, thereby improving the accuracy of fire point alarm.

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.

The present invention provides a method, system, terminal, and medium for aggregating multi-target REDD project areas, specifically relating to the field of forest carbon sequestration technology. This solution constructs a multidimensional weighted feature set for each coordinate point based on the collected coordinate points within several target REDD project areas and the geographic feature data of the target layer at each coordinate point, as well as preset spatial coordinate aggregation weights and preset geographic feature aggregation weights. A clustering algorithm is used to cluster all the multidimensional weighted feature sets to obtain grid point sets corresponding to the multidimensional weighted feature sets. A convex hull algorithm is used to calculate the convex hull of the coordinate points in each grid point set to obtain the convex hull set of the grid point sets. Based on the target REDD project areas corresponding to all convex hull sets, an aggregated region for all target REDD project areas is obtained. This solution can effectively improve the accuracy of REDD project area boundary identification, thereby facilitating the comprehensive management and assessment of dispersed areas.

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.