131 results about "Geographic feature" patented technology

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention relates to the technical field of machine vision, and discloses a surveying and mapping geographic information analysis method and system based on machine vision and a medium. The method comprises the following steps: preprocessing multi-source remote sensing data to obtain a standardized surveying and mapping data set, and extracting ground feature edge and texture information to obtain a multi-scale spatial feature and a semantic segmentation result; carrying out multi-source data integration to obtain a unified geographic element representation diagram, and carrying out geographic element identification on the unified geographic element representation diagram to obtain a surveying and mapping geographic element set; performing Bayesian inference of spatial uncertainty quantization and parameter uncertainty quantization on the surveying and mapping geographic element set to obtain a comprehensive uncertainty evaluation result; and constructing vectorized geographic information based on the surveying and mapping geographic element set and the comprehensive uncertainty evaluation result. According to the method and the device, the edge information of the geographic elements is effectively reserved, the identification precision of the geographic elements is improved, the geographic information expression is more structured, and automatic updating is realized.

The invention discloses a channel electronic fence calculation method and system based on an unmanned aerial vehicle. The method comprises the following steps: setting a channel basic fence according to geographic features of a channel; setting a lowest fence standard according to ship attributes; fusing the navigation channel basic fence and the lowest fence through a label priority mechanism to obtain a navigation electronic fence of the ship; acquiring channel monitoring data through an unmanned aerial vehicle group to obtain a real-time monitoring label; and dynamically adjusting the navigation electronic fence based on a preset rule engine and the real-time monitoring tag. According to the invention, dynamic adjustment of fence parameters is realized through linkage of the real-time monitoring label and the rule engine in cooperation with the unmanned aerial vehicle group; customizing a core protection area according to different types, loading states and maneuvering performance of cargo ships, passenger ships and the like; by constructing a multi-dimensional label system, multi-source data is converted into labels with unified semantics, and deep data fusion is achieved; and a machine learning model is introduced to dynamically calibrate the label, and a label system and a calibration rule are optimized.

The invention relates to an unmanned aerial vehicle flight path planning method based on semantic guidance and visual perception, and the method comprises the following steps: S1, obtaining original image information and a digital elevation model, and generating a standardized target region image, S2, inputting the standardized target region image into a trained improved U-Net semantic segmentation model, the improved U-Net semantic segmentation model outputs a pixel-level geographic feature classification result, S3, according to the geographic feature classification result, determining an obstacle and a passable area, and generating a binary grid map, the obstacle area being marked as 0, and the passable area being 1, and S4, generating an initial flight path based on the binary grid map, and if the initial flight path is blocked, executing the step S3. Triggering an online re-planning algorithm to generate an alternative path; the method has the advantages that lightweight calculation and high robustness are both considered, and the flight safety and the real-time decision-making capability in a complex scene are remarkably improved.

The invention discloses a spectrum allocation method and device based on graph data and deep reinforcement learning and a medium, and belongs to the field of electric power communication networks, and the method comprises the steps: fusing the multi-dimensional node features of an electric power communication air-ground ad hoc network, and calculating the weight of channel quality and interference coupling, thereby obtaining a spectrum allocation result of the electric power communication air-ground ad hoc network; constructing a dynamic graph structure model of a node and link relationship; constructing a time-space associated multi-dimensional spectrum map by fusing the geographic feature data, the frequency band service data, the channel feature data and the version information data; and on the basis of the dynamic graph structure model and the multi-dimensional spectrum map, coding dynamic topological characteristics through a graph convolutional network, and combining experience playback and a dynamic Q value updating mechanism, generating a spectrum allocation action meeting a power constraint and an interference threshold, and executing spectrum allocation of the electric power communication air-ground ad hoc network according to the spectrum allocation action. Therefore, by implementing the method, the robustness of spectrum allocation of the electric power communication air-ground ad hoc network can be improved.

The invention discloses a method for constructing a computable road network. Road data in the vector map are extracted, line string data or line segments obtained by splitting the line string data are used as connecting edges, geographic feature codes containing Geohash information are given to starting points and ending points of the connecting edges, and therefore a basic road network map is constructed. And extracting a middle vertex set of the basic road network diagram and an edge attribute set of middle vertex connection edges, and carrying out road network vertex optimization based on edge attributes to obtain an optimized computable road network. Geographic feature codes adopted by the method comprise spatial geographic information and have the characteristics of graph vertex coding, and the constructed road network graph can be used for graph calculation and verification and restoration of road connection relations. Furthermore, in combination with a representation point acquisition scheme provided by the invention, a road network map fusing facilities and equipment can be constructed, so that the application range of the computable road network is expanded. The computable road network constructed by the method is high in operation efficiency and suitable for different production environments.

The invention relates to the field of knowledge maps, and discloses a method and system for processing and predicting enterprise address data, and the method comprises the steps: building a knowledge base through cleaning multi-source heterogeneous address data, training an embedded enhancement model, carrying out the multi-granularity word segmentation through employing a Word-Pi ece algorithm and a field extension dictionary, and carrying out the prediction of the enterprise address data. A result is input into the model to generate an initial hierarchical label sequence, and after verification and completion of the place name recognition model, a standardized structured address is output based on a priority fusion rule; the system comprises a data cleaning and knowledge base construction module, a hierarchical prediction model training module, a multi-granularity word segmentation module, a hierarchical prediction module, a hierarchical verification and fusion module and a distributed computing cluster. The analysis accuracy is improved by fusing semantic and geographic features, and the dynamic knowledge base is combined to adapt to administrative division change; the processing efficiency is optimized by adopting a distributed calculation and acceleration technology; manual intervention is reduced through automatic verification, and standardized address data is generated.

The invention discloses a GIS-based cross-section diagram real-time synchronous display method and system, and relates to the technical field of geological modeling, and the method comprises the steps: importing multi-source heterogeneous geographic data, carrying out the standardized preprocessing, and generating a geographic information data set in a unified format; carrying out multi-dimensional feature extraction on the geographic information data set by adopting a feature level fusion algorithm, and constructing a multi-dimensional geographic feature model fusing geological structure, terrain elevation and earth surface coverage information; performing data optimization by adopting a spatial interpolation algorithm based on the multi-dimensional geographic feature model to generate a continuous geographic surface model; and performing parallel contour tracking and topological sorting on the discrete section line segment set, and generating a user-defined section diagram supporting interactive parameter adjustment in real time. According to the method, the multi-dimensional geographic feature model is constructed through the feature level fusion algorithm, semantic level association of the geologic structure, the terrain elevation and the earth surface coverage information is realized, the problem of pixel level fusion is solved, and the data space consistency error is reduced.

The invention discloses a three-dimensional geographic scene dynamic updating method, device and equipment based on space-time interpolation and a medium, and the method comprises the steps: collecting multi-source heterogeneous data, including a satellite image, an unmanned plane remote sensing image and ground sensor data; calculating the weight of a single data source according to the spatial resolution and the time resolution of the data; carrying out spatial interpolation and time interpolation calculation on spatial points of the three-dimensional scene in combination with the multi-source data weight; performing nonlinear compensation on the spatial interpolation and the time interpolation to obtain a compensated spatial point update value of the three-dimensional scene; and comparing interpolation results at different time points, detecting local change, and updating the change area of the three-dimensional scene. According to the method, the fusion quality is guaranteed through reasonable weights, and the weights are configured by integrating spatial resolution, time resolution, data quality and reference data similarity, so that three-dimensional scene basic data fusion is more fit for real geographic features.

The invention discloses a remote sensing image cloud type inversion model and system based on symmetric cross attention. The method comprises the following steps: acquiring satellite remote sensing image data, and extracting cloud spectrum, texture and shape features after processing; performing feature coding on longitude and latitude coordinates, terrain, underlying surfaces and time elements; constructing a symmetric cross attention module, and bidirectionally fusing the image, the geographic feature and the time feature as channel input; a ClodSat cloud product is used as a truth value, an Adam optimizer and a cross entropy loss function training model are adopted, and verification is carried out through indexes such as a Kappa coefficient and producer precision. The method overcomes the defect that a traditional method only depends on two-dimensional images and does not consider external driving elements, cloud types can be accurately classified, and inversion accuracy and efficiency are improved.

The invention relates to the field of image content understanding, and discloses an image geographic positioning system and method based on geographic feature extraction, and the system comprises a feature extraction module which is used for extracting a visual feature vector, a GPS feature vector, a text position description feature vector and a text scene description feature vector from multi-modal input; the comparative learning module is used for realizing multi-modal feature alignment through comparative learning of the visual features, the GPS features, the text position description features and the text scene description features; and the data set construction module is used for fusing the multi-modal features to generate geographic feature vectors and constructing a retrieval vector data set. By adopting the technical scheme of multi-modal feature fusion and cross-modal comparative learning, the technical effect of improving the geographic positioning precision and generalization ability is achieved. Compared with a scheme depending on a single mode or simple feature splicing in the prior art, the problem of semantic gaps caused by mode information splitting in a traditional method is solved.

The invention relates to an animal epidemic disease monitoring data statistical analysis system and method. The method comprises the following steps: a multi-source data fusion module performs standardized fusion processing based on input case data, environment data and population data to obtain a spatio-temporal joint data matrix; a risk probability prediction module performs area scanning shape prediction on geographic feature data and population distribution data in the matrix, outputs a dynamic scanning area set, and performs environment and small sample correction to obtain risk probability distribution; and the hierarchical prevention and control visualization module performs spatial aggregation test based on the distribution, outputs a significant epidemic disease aggregation area, and generates a hierarchical prevention and control thermodynamic diagram in combination with an environment correction factor. According to the system, through multi-source data fusion, geographically adaptive dynamic window prediction and spatial aggregation test, the animal epidemic disease risk probability can be accurately analyzed, an epidemic disease aggregation area is output, a hierarchical prevention and control thermodynamic diagram is generated, and the animal epidemic disease monitoring level and the prevention and control visualization capability are further improved.

The invention relates to a flood prevention drainage method and device based on a digital intelligent scheduling algorithm, and relates to the technical field of flood prevention drainage. A relay; the number of the relays corresponds to that of the water pumps; the touch screen all-in-one machine is used for man-machine interaction and displaying the running state of the drainage device The liquid level sensor is used for detecting the water level height in real time; a control mainboard; the control mainboard is electrically connected with the touch screen all-in-one machine, the relay, the liquid level sensor, the variable frequency driver and the current sensor; the control mainboard obtains a weather condition sent by the external equipment; the control mainboard predicts the water level change trend through a water level prediction model based on the water level height, the weather condition and the geographic features of the drainage area and generates a drainage task. And the control main board determines the working sequence of all the water pumps performing the drainage task according to the drainage flow, the energy consumption and the current operation duration of each water pump, and controls the relays of the water pumps in the working state to be in the conduction state.

The invention relates to the technical field of airport noise prediction. The airport noise map isoline calculation method and system based on multi-source data fusion are provided, and the method comprises the following steps: carrying out space-time alignment fusion processing on multi-source heterogeneous data to generate a space-time synchronous standardized data set; processing through a track-noise coupling calculation unit to generate a single event noise value containing a meteorological correction factor; performing dynamic gridding accumulation calculation processing on the single event noise value to generate a noise evaluation quantity grid matrix with a time stamp; processing is carried out through a curvature-driven adaptive contour line generation unit, and a fidelity-optimized noise contour map is generated; and selecting a real-time lightweight channel or a distributed batch processing channel for processing based on the calculation mode decision-making unit, and outputting a noise contour map with optimized fidelity, so as to achieve the technical effects of improving noise positioning precision, reducing modeling errors in a dynamic flight environment and improving the fidelity of geographic features of contour lines.

The invention discloses a coastal disaster forecasting method and system. The method comprises the following steps: acquiring data of a target near-shore area, and carrying out data preprocessing and unstructured grid division; generating an open sea drive-near-shore response sample library by using the coupling numerical model; extracting the dominant lag step number of each open-sea factor for each node near the shore through mutual information analysis, performing time rearrangement on key factors, splicing with static geographic features, and constructing a structured input tensor; training a graph attention space-time fusion model taking the unstructured grid as a graph structure; in real-time forecasting, an implicit state assimilation mechanism based on observation errors is introduced, and the model state is dynamically corrected to achieve self-adaptive rolling forecasting. According to the invention, high-precision and high-efficiency near-shore disaster real-time early warning is realized.

The invention discloses a disaster risk early warning method and system combined with multi-source data analysis and electronic equipment, and relates to the technical field of data analysis. The method comprises the following steps: carrying out monitoring range screening according to real-time latitude and longitude coordinates to obtain K disaster risk monitoring subareas; executing multi-source information cross-platform crawling to obtain K pieces of disaster monitoring multi-source data; retrieving and outputting the geographic feature data of the K subareas from the open GIS data platform; acquiring disaster risk characteristics of K subareas; carrying out risk dispersion fitting on the disaster risk characteristics of the K subareas, and outputting disaster risk evolution characteristics; and performing spatial consistency analysis according to the disaster risk evolution characteristics and the position deviation of the real-time latitude and longitude coordinates, outputting real-time disaster risk early warning, and transmitting the real-time disaster risk early warning to a risk monitoring user through a mobile terminal. The technical problems of lagged response and insufficient accuracy of disaster risk early warning in the prior art are solved, and the technical effect of improving the timeliness and accuracy of disaster early warning is achieved.

The invention provides a railway Beidou satellite signal simulation method and system based on artificial intelligence. The method comprises the steps of obtaining geographic data corresponding to a railway simulation line and ephemeris data corresponding to simulation time; through statistical analysis of geographic data, geographic features such as geometric features, terrain categories, geotechnical engineering parameters, geologic structure parameters, stratigraphic structure parameters and ground feature distribution features of the terrain are obtained; input of a pre-trained artificial intelligence model is obtained based on the geographic features and ephemeris data, so that multi-dimensional analog signal parameters are obtained; and performing navigation message filling by using the ephemeris data to generate a navigation message, and generating a Beidou satellite simulation signal based on the navigation message and the multi-dimensional simulation signal parameters. According to the invention, efficient, accurate and intelligent analog signals can be generated.

A method of providing a map with imbedded, authenticated data is described. A geographic feature is represented on a map with one or more of points, lines and polygons in a first layer of the map. Data corresponding to the geographic feature is provided. The provided data is encoded, in a first block, with the one or more of points, lines and polygons from the first layer of the map. The block is combined with other blocks of a blockchain. The blockchain is associated with the geographic feature on the map as represented by the one or more of points, lines and polygons in the first layer of the map.

The invention discloses an unmanned aerial vehicle route global planning method based on key feature points, and relates to the field of unmanned aerial vehicle route planning, and the method comprises the steps: carrying out the hierarchical division of a three-dimensional elevation map according to the performance of an unmanned aerial vehicle; according to obstacle pixel proportions of different layers, an optimal layer is screened out; on the basis of the segmentation maps of the optimal layer and the adjacent layer and in combination with the three-dimensional geographic features, obtaining corrected key feature points; utilizing a heuristic weight calculation formula to calculate heuristic weight values of the corrected key feature points; according to a sorting result of the heuristic weight values, a plurality of corrected key feature points of the unmanned aerial vehicle route are obtained according to a connection barrier-free principle and in combination with a three-dimensional path algorithm until a target point is selected; and based on the initial point, the target point and the plurality of corrected key feature points of the unmanned aerial vehicle route, generating an optimized unmanned aerial vehicle flight path after corner optimization. According to the method, the three-dimensional feasible flight route can be efficiently generated, and the calculation time is short.

The invention relates to the technical field of data processing, and discloses an atmospheric pollutant emission data processing method, which comprises the following steps: SS1, data collection and distribution diagram generation: collecting multi-source meteorological real-time data, multi-source meteorological prediction data, multi-source geographic feature data, human activity data and historical pollutant data of a pollutant atmospheric pollution monitoring area in a classified manner, and generating distribution diagrams according to the multi-source meteorological real-time data, the multi-source meteorological prediction data, the multi-source geographic feature data, the human activity data and the historical pollutant data; the collection frequency is set according to the characteristics of different data sources, preprocessing and data fusion are carried out on the collected multi-class data, and finally a real-time distribution diagram of pollutants is generated. According to the method, multi-source data such as meteorological data, geographic data and human activity data are comprehensively collected, deep preprocessing and fusion are carried out, an accurate pollutant real-time distribution diagram is generated, the pollutant type and concentration of each monitoring point position can be visually presented, meanwhile, the weight is constructed by comprehensively considering multiple factors, and the real-time distribution diagram of the pollutants is obtained. The diffusion path and concentration of the pollutants are predicted by combining the diffusion model based on the physical equation and the prediction model based on the neural network, and the prediction precision is high.

The invention belongs to the technical field of air pollutant traceability. The invention provides an air pollution prediction method and system based on artificial intelligence and a mechanism model, and the method comprises the steps: extracting geographic features and time dependency relationship features, fusing the geographic features and the time dependency relationship features, outputting potential calculation factors, and calculating the weight of each factor; a high weight factor with the absolute value of the weight larger than a set threshold value is fused into the mechanism model, the reference emission rate of the mechanism model serves as basic input data, the selected high weight factor and the weight are combined to calculate the corrected gridding emission rate, and an improved mechanism model with a dynamic emission source list is formed; the pollutant concentration is predicted through the improved mechanism model, and the main pollution source is determined based on pollution source data and potential calculation factors in the database; according to the method, various factors involved in the atmospheric pollution forming process are fully considered, and the prediction precision is improved.

The invention relates to the technical field of geographic surveying and mapping, and discloses a geographic surveying and mapping method and system based on unmanned aerial vehicle remote sensing, and the geographic surveying and mapping method based on unmanned aerial vehicle remote sensing comprises the following steps: processing source domain topographic data and target domain scarce samples through a feature selective migration network, and obtaining a feature adaptability model parameter set; processing a low-resolution remote sensing image through a physical constraint generative adversarial network to obtain a high-resolution geographic feature enhanced image data set; processing the high-resolution geographic feature enhanced image data set and the feature adaptability model parameter set through a hierarchical attention network to obtain a fine-grained ground feature classification result matrix; through a small sample learning strategy of feature selective migration, a super-resolution reconstruction technology of physical constraint, a fine-grained classification method of hierarchical attention and a standardized geographic data product generation process, the dependency of surveying and mapping data acquisition on the environment is reduced, the surveying and mapping efficiency is improved, and the surveying and mapping precision is improved.

The invention relates to the technical field of project cost, and discloses a method, a system, equipment and a medium for precisely checking a cost range based on geographic region characteristics, and the method comprises the steps: collecting project engineering data, and carrying out the data preprocessing; performing feature extraction based on the project engineering data to obtain project geographic features and engineering cost features, and performing feature analysis; constructing a project construction cost model, and calculating to obtain a project construction cost prediction range; and comparing the construction cost with a preset construction cost range to check the construction cost, judging whether the construction cost exceeds the preset construction cost range or not, and adjusting and optimizing the project construction cost if the construction cost exceeds the preset construction cost range. According to the method, the cost range of various engineering projects in different geographic areas can be predicted, the prediction precision is improved, through comparison with actual cost data, the prediction accuracy is evaluated by adopting a statistical method, the high accuracy of the final cost range is ensured, and a visual and comprehensive decision basis can be provided for a decision maker according to a cost check result.

The present specification relates to the technical field of satellite, and particularly relates to a satellite traffic flow prediction method and device. The method comprises: acquiring geographical feature data of a wave position; acquiring coverage time data of the satellite for the wave position in a historical period; constructing first feature data of the satellite according to the geographical feature data and the coverage time data; the first feature data is used for representing geographical features cumulatively covered by the satellite in the historical period; acquiring second feature data, the second feature data is used for representing traffic flow of the satellite in the historical period; and predicting future traffic flow of the satellite according to the first feature data and the second feature data. The embodiment of the present specification can improve prediction accuracy.

An information processing apparatus calculates a difficulty level of a predetermined route and includes one or more processors and one or more memories communicably coupled to the one or more processors. The one or more processors are configured to: determine an attribute of the predetermined route; acquire information about a plurality of routes each having an attribute similar to the determined attribute; calculate a first probability distribution, indicating an occurrence tendency of a value indicated by at least one first indicator related to a geographical feature in all of the routes, based on the acquired information about the routes; and calculate the difficulty level of the predetermined route based on a position, in the first probability distribution, of the value indicated by the at least one first indicator at a predetermined point or in a predetermined section included in the predetermined route.

This invention relates to the field of human-computer interaction interface management technology, and provides a method and system for adaptive adjustment of interface style based on a 3D car model. It identifies multi-dimensional attribute features such as geographical features, user features, and holiday features in the current scene in real time, and achieves style conversion through style scoring and differentiated adjustments, controlling the adaptive adjustment of the 3D car model's interface style. On the one hand, by integrating user features, geographical features, and holiday features, it achieves dynamic adaptive adjustment of the 3D car model's style, enabling flexible switching between multiple preset base models while balancing personalized expression and rendering efficiency. On the other hand, it differentiates the basic style of the target base model based on multi-dimensional attribute features, performs style conversion through a model style generation engine, and dynamically renders in real time to achieve intelligent repainting of textures that flexibly change with the scene.

The invention discloses a new energy unit icing shutdown prediction method and system based on a multi-feature interaction threshold. The method comprises the steps of collecting and preprocessing multi-dimensional data of a new energy unit; based on meteorological and geographic features, generating a corresponding clustering label for each new energy unit by using a clustering algorithm; constructing a full-connection deep neural network shutdown prediction model, taking the preprocessed data and the clustering labels as input features, and training the icing shutdown probability of a model output unit; for a single feature, a feature fixing strategy is adopted, and a single feature threshold interval is determined; key features are selected for double-feature interaction analysis, a three-dimensional decision boundary is constructed, an interaction effect is quantified, and a multi-feature interaction rule is extracted based on a decision tree algorithm; and constructing a comprehensive discrimination rule, setting risk preference parameters, carrying out adaptive threshold updating, and finally outputting a shutdown prediction result. The method can significantly improve the shutdown prediction precision, and is suitable for different types of new energy equipment such as wind power and photovoltaic equipment.

Embodiments of the present specification provide a method and device for discovering changes in geographic features and a storage medium. The method comprises: obtaining new acquisition data of a target geographic area, the new acquisition data comprising new trajectory data and new image data; associating the new trajectory data with the new image data to obtain new trajectory-image association data; obtaining historical trajectory-image association data of the target geographic area; and determining whether there is a changed geographic feature in the target geographic area based on the new trajectory-image association data and the historical trajectory-image association data.

The application discloses a substation site selection geographic information identification method, system, device and medium, comprising: performing geographic feature extraction on first geographic data of a substation planning area to obtain geographic information features, and performing site selection constraint quantization based on the geographic information features to obtain substation site selection constraints; inputting second geographic data of the substation planning area into a semantic segmentation model to obtain a target suitable construction area in the substation planning area output by the semantic segmentation model; performing area screening on each position space area in the target suitable construction area based on the substation site selection constraints to obtain a target position space area; and performing site selection point screening on each site selection point in the target position space area based on a power shortage area to obtain a final substation site selection point. The application guarantees the rationality of substation site selection and improves the reliability of substation site selection.

The application discloses a kind of coal mine fully mechanized working face geographic feature acquisition system and method, belong to coal mine information acquisition technical field, feature acquisition system includes the detection device being arranged in working face along line area and the UWB positioning base station being fixed in working face along line area, detection device includes: image acquisition equipment, for collecting the image information of working face along line area;Symmetrically arranged horizontal rotary laser ranging equipment and vertical rotary laser ranging equipment on the two sides of image acquisition equipment.The application adopts the double positioning method based on UWB positioning and gyroscope motion positioning and laser ranging technology, and the geographic features and cutting coal wall features of image are described by detection device, which increases geographic information and three-dimensional distance information on the basis of plane visualization for coal mine safety production, and provides the required monitoring and detection conditions for improving safety production for coal mine working face site unattended production.