81 results about "Geographic feature" patented technology

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 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 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.

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.

The application belongs to the technical field of deep learning and remote sensing image processing, and specifically discloses a city land use mapping method based on multi-modal data collaborative perception, which comprises the following steps: performing parcel division on high-resolution remote sensing images of a research area to obtain irregular city parcels and remote sensing spectral features thereof; setting street sampling points along a road network, extracting street perception features and extracting interest point semantic features; constructing a heterogeneous graph structure based on the street sampling points, the street perception features and the interest point semantic features falling into the same parcel, and then extracting parcel-level multi-source geographic features; inputting the remote sensing spectral features and the parcel-level multi-source geographic features into a full sparse topic model for semantic alignment and fusion to generate fused parcel feature representations; and performing classification based on the fused parcel feature representations to output city land use mapping results. The application can realize high-precision and high-robustness city land use recognition under the condition that multi-source data is unevenly distributed or sparse.

The application provides a place recommendation system, comprising an encoding module, a sequence period feature extraction module, a geographical feature extraction module and a query mapping module. The encoding module is used for encoding original data to form a user sequence. The sequence period feature extraction module comprises a time interval feature extraction module for extracting interval rules in the user sequence, a time span feature extraction module for extracting time span rules in the user sequence, and an aggregation module for aggregating the interval rules and the time span rules. The geographical feature extraction module is used for learning place features at different geographical scales. The query mapping module is used for place recommendation according to the sequence period features and the place features. The system captures the period features of multiple time intervals and multiple time spans in the user sequence, extends to a physical geographical map, adds geographical feature information to the place recommendation, and effectively improves the reliability of the recommendation result.

The present application belongs to the technical field of sea surface temperature prediction, and particularly relates to a multi-scale physical information fusion large language model sea surface temperature prediction method and system; the method comprises the following steps: inputting historical sea surface temperature data, preprocessing, generating sea temperature input data fused with spatial geographic features, and performing multi-level and multi-scale spatial and temporal feature extraction to obtain physical features; obtaining a pre-constructed marine field knowledge library, obtaining word vectors from a pre-trained language model, linearly mapping text semantic features, fusing the physical features and the text semantic features in the same feature space to obtain joint semantic features; constructing prompt word vectors related to the prediction task, inputting the joint semantic features into a large language model, guiding the model to perform deep feature interpretation and prediction through the prompt word vectors, and outputting the prediction result of the future sea temperature field; thereby the complex spatio-temporal change information of the sea temperature can be mined, and high-precision spatio-temporal prediction of the sea temperature field can be realized.

The invention provides a space-time intelligent analysis system and method for watershed pollution based on deep learning, and the system monitors the total amount of pollutants in each region in real time through a fixed water quality monitoring station, employs the total amount as a constraint observation value of space distribution, and obtains multispectral dynamic feature data and static geographic feature data through the remote sensing of an unmanned plane. The method comprises the following steps: aligning and fusing multi-source spatial data in a unified space-time coordinate system, constructing a multi-dimensional spatial feature matrix of a high-resolution grid, establishing a model by using a convolutional neural network, spatially distributing the total amount of pollutants, predicting the pollution contribution amount of each grid, and generating a pollution source distribution thermodynamic diagram. According to the method, a high-precision and high-resolution pollution source space-time distribution analysis result can be provided for drainage basin pollution treatment, reliable support is provided for precise and scientific environment management decisions, and development of intelligent environmental protection and drainage basin ecological protection is powerfully promoted.

This invention discloses an automated fusion method and system for suspended sediment concentration from multi-source ocean color satellites, belonging to the field of marine remote sensing and geographic information processing technology. The method includes: S1, automatically identifying and reading the original satellite data file; S2, processing the quality labeling data to generate a comprehensive quality mask in one go, and marking invalid values ​​for the concentration data; S3, resampling all valid pixels onto a predefined regular geographic grid, calculating the cumulative concentration and pixel count of each grid cell to obtain the grid mean; S4, stacking multiple grid data from the same region and time window along the time axis, and performing spatiotemporal fusion using the median robust averaging method or the minimum value synthesis method; S5, automatically generating a standard raster file and a thematic map containing geographic features and embellishment information based on the fused data. This invention solves the problems of fragmented processing, low efficiency, and insufficient accuracy in existing technologies.

The invention discloses a seasonal change monitoring method and system based on artificial intelligence. According to the scheme, regional geographic information of a monitoring region, a historical monitoring data sequence in at least one historical monitoring period and acquisition time information of the historical monitoring data sequence are acquired; carrying out feature deep mining on the regional geographic information and a historical monitoring data sequence in a historical monitoring period; based on reference depth monitoring feature information and depth monitoring feature information of a reference area around the monitoring area in a historical monitoring period and acquisition time information of a historical monitoring data sequence, seasonal feature analysis is carried out on the monitoring area; obtaining seasonal change characteristics of the monitoring area in different monitoring periods, and predicting a seasonal change result of the monitoring area by adopting a machine learning model according to the depth monitoring characteristic information, the seasonal change characteristics and the depth geographic characteristic information; according to the scheme, the seasonal change of the region can be predicted by adopting an artificial intelligence technology, so that the seasonal change monitoring accuracy is improved.

The invention relates to the technical field of logistics planning, and discloses a distribution path planning method and device for a cross-border region, computer equipment and a storage medium, and the method comprises the steps: obtaining cultural taboo data, real-time geographic feature data and real-time meteorological data of a target region; constructing a cultural taboo space-time grid based on the cultural taboo data; road health indexes are calculated according to the real-time geographic features and the meteorological data, and a geographic road health index map is generated; and after a delivery order is received, outputting a delivery strategy according to the order attribute data, the cultural taboo space-time grid and the road health index map based on a pre-trained large language model, generating a hierarchical path scheme corresponding to the delivery strategy according to a preset path planning algorithm, and finally pushing the hierarchical path scheme to a deliveryman terminal. According to the method, a dual constraint mechanism of cultural taboo and road conditions is established, so that the problems of distribution conflicts and low distribution efficiency caused by neglecting regional cultural customs and dynamic road conditions in traditional path planning are effectively solved.

Systems and methods for facilitating travel of an electric off-road vehicle on an uncharted off-road route are provided. A method includes relating the uncharted off-road route to be travelled by the electric off-road vehicle to map data of a geographic area including the uncharted off-road route. Using the map data, one or more geographic characteristics of the uncharted off-road route are determined. Using the one or more geographic characteristics of the uncharted off-road route and predetermined battery consumption data associated with the one or more geographic characteristics, an estimated battery consumption for the electric off-road vehicle to travel the uncharted off-road route is determined. The estimated battery consumption is communicated to an operator of the electric off-road vehicle.

The invention discloses a map region division method, a map clock-in method, a map clock-in device and a computer program product. The map region division method comprises the following steps: acquiring segmentation geographic elements in a map region to be divided; dividing the to-be-divided map area into a plurality of preliminary divided areas according to the divided geographic elements; selecting areas meeting merging and screening conditions from the plurality of preliminarily divided areas to obtain a plurality of areas to be merged; and according to a preset merging mode, merging the mutually associated to-be-merged regions in the plurality of to-be-merged regions to obtain a region division result corresponding to the to-be-divided map region. Through the scheme provided by the invention, different areas can be provided for users more accurately and finely, and when the method is applied to area card punching, footprint differences among different users can be well displayed.

The application provides a high-cold steep slope ecological restoration scheme recommendation method and system based on machine learning, historical restoration case data sets and regional geographic feature data of a high-cold steep slope region are collected, the historical restoration case data sets and the regional geographic feature data are associated and fused to generate a regional ecological restoration feature map with spatio-temporal correlation, a pre-trained restoration scheme optimization model is called to perform multi-dimensional adaptability analysis and processing on the regional ecological restoration feature map to obtain an ecological restoration scheme adaptability score matrix of the high-cold steep slope region, and based on the ecological restoration scheme adaptability score matrix and the regional ecological restoration feature map, an ecological restoration scheme spatial recommendation distribution map of the high-cold steep slope region is generated. The application improves the accuracy and practicability of the ecological restoration scheme recommendation.

The invention provides a medium-voltage power distribution network intelligent planning method and system based on dynamic geographic characteristics, and the method comprises the steps: carrying out the mode recognition based on the current power grid structure and load distribution information of a medium-voltage power distribution network, and obtaining a typical planning problem and the corresponding problem information; performing feasible path search based on the dynamic geographic feature data of each region covered by the medium-voltage distribution network and the current grid structure of the power grid to obtain an initial feasible path; screening the initial feasible paths based on preset path constraints to obtain candidate feasible paths; performing network topology analysis based on the candidate feasible path, the typical planning problem and the corresponding problem information, and constructing a target power grid structure; and performing simulation operation on the target power grid structure based on different working conditions to obtain a simulation operation result, and performing optimization adjustment on the target power grid structure according to the simulation operation result to obtain a power grid planning result. The medium-voltage distribution network planning efficiency and the planning result quality are improved.

The invention discloses a power supply battery state-of-charge measurement method and device for meteorological equipment, and relates to the field of battery management. The method comprises the following steps: acquiring real-time operation parameters of a power supply battery of the meteorological equipment and environmental parameters of an environment where the meteorological equipment is located; based on the real-time operation parameters, calculating to obtain an initial battery state-of-charge value; acquiring historical operation parameters and historical environment parameters of the meteorological equipment, and constructing a geographic feature vector based on the historical operation parameters and the historical environment parameters; according to the geographic feature vector, determining a trigger condition, and judging whether the environmental parameters meet the trigger condition; if it is determined that the environmental parameters meet the triggering conditions, dynamic calibration is executed based on the geographic feature vector and the initial battery charge state value to calculate a charge state correction value; and synthesizing the state-of-charge correction value and the initial battery state-of-charge value to obtain a final battery state-of-charge value. By implementing the technical scheme provided by the invention, the measurement accuracy of the state of charge of the power supply battery is improved.

This invention belongs to the field of UAV navigation and augmented reality technology, and relates to a passive geographic information overlay system and method based on cross-modal features. The system includes: an airborne visual acquisition module installed on the UAV platform for real-time acquisition of video frames and flight attitude data; an edge computing processing module for receiving video frames and performing geographic information calculations, calculating confidence levels, and outputting geographic feature information that meets a set confidence threshold as a preferred dataset; and an augmented reality display module for overlaying the geographic feature information from the preferred dataset onto the video image in an augmented reality manner. Its advantages include the ability to perform positioning matching and augmented display without relying on satellite signals in GNSS-denied environments, strong anti-interference capabilities, high positioning accuracy, and low overlay latency. It can overlay and present geographic features in UAV videos, facilitating users to quickly identify and understand the corresponding geographical location and environmental elements in the image.

The application 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, obtaining a standardized surveying and mapping data set, extracting ground object edges and texture information, obtaining multi-scale spatial features and semantic segmentation results, integrating multi-source data, obtaining a unified geographic feature representation map, identifying the unified geographic feature representation map, obtaining a surveying and mapping geographic feature set, performing Bayesian inference of spatial uncertainty quantization and parameter uncertainty quantization on the surveying and mapping geographic feature set, obtaining comprehensive uncertainty evaluation results, and constructing vectorized geographic information based on the surveying and mapping geographic feature set and the comprehensive uncertainty evaluation results. The application effectively retains the edge information of geographic features, improves the recognition accuracy of the geographic features, makes the geographic information expression more structured, and realizes automatic updating.