994 results about "Geographic area" patented technology

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

The invention relates to the technical field of building monitoring, and provides an early warning method and system for pile foundation health state analysis. The method comprises the steps that a monitoring platform receives and processes various monitoring data from a multi-source sensor in real time, and data fusion and dynamic trend analysis are carried out on the various monitoring data by combining a graph neural network and a lightweight multi-head attention sequential network with a building structure of a target building to obtain an initial pile foundation evaluation result; uploading the initial pile foundation evaluation result of the target building in each geographic area and various monitoring data to a cloud data processing center; the cloud data processing center stores the initial pile foundation evaluation results of the target buildings in the geographic areas, and comprehensively evaluates the correlation trend between the target buildings in the geographic areas to obtain correction parameters; and the correction parameters are issued to a monitoring platform, the monitoring platform dynamically combines the correction parameters to update the pile foundation health state of the target building in each geographic area, and a target pile foundation evaluation result of the target building is obtained.

The invention relates to a method for the determination of the carbon balance in a predetermined geographic area, applicable to natural and / or human-managed systems, which integrates a series of known and validated technologies into a single information system on the carbon sequestered by natural and human-intervened systems. A system for determining the total carbon balance of a predetermined geographic area is also provided.

In various examples, systems and methods are described that may be used to generate a mask of a geographic area and corresponding vector representations of one or more environmental features included in the area. In some embodiments, the method and system may generate or obtain a first tile image representing a portion of a path surface corresponding to a geographic area. One or more features may be extracted from the data where the extracted features may indicate environmental characteristics associated with the path surface—e.g., lane boundaries, medians, traffic signs, signals, etc. Additionally, the method or system may generate a mask corresponding to the tile image and vector representations of the portion of the geographic area. In some embodiments, the locations of the environmental features associated with the mask may be reconciled with one or more previously generated masks that include some of the same environmental features.

The invention relates to the technical field of image detection, in particular to a satellite and unmanned aerial vehicle cooperative remote sensing image change detection system and method, and the method comprises the steps: obtaining a dual-time-phase satellite remote sensing image and an unmanned aerial vehicle remote sensing image of the same geographic region; the satellite branches are subjected to four-stage convolution-pooling operation, multi-stage wide-area features are output, and the unmanned aerial vehicle branches output high-resolution local features aligned with the satellite branches in space through the backbone network; performing channel interaction operation on each stage of double-time-phase features, and generating a spatial weight map to strengthen a change region; aligning adjacent level resolutions through transposition convolution, dynamically integrating multi-scale features, and outputting optimized fusion features in combination with residual connection; and performing convolution dichotomy on the fused features to generate a change detection result graph. According to the method, the problem of signal attenuation caused by heterogeneous data feature mismatch is effectively solved, the false alarm rate caused by environmental interference is remarkably reduced, and the complete detection capability of a micro-to-macro full-scale dynamic target is synchronously improved.

The embodiment of the invention provides a forest type remote sensing classification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target geographic area and collecting multi-source remote sensing data; extracting canopy multi-dimensional space-time spectrum features based on multi-source remote sensing data, and screening geographical environment covariables capable of representing forest type differences; calculating the overall geographical environment similarity of the unknown point and the sample point by using a Gaussian similarity function; quantifying the individual representativeness of the sample points to the unknown point according to the overall geographical environment similarity, and selecting the forest type represented by the sample point with the highest individual representativeness as the forest type of the unknown point; drawing a forest type spatial distribution diagram and a classification uncertainty distribution diagram of the target geographic area based on classification results of forest types of all unknown points; and performing precision verification on the forest type spatial distribution map based on the classification uncertainty distribution map and a preset precision evaluation index.

A facility for estimating the value of a distinguished home is described. The facility trains a forest of decision trees to estimate valuations for homes within the geographic area where the distinguished home is located using data including both previous home sale transaction prices and synthetic sale transaction prices based on listing prices. The facility accesses information about the distinguished home's attributes and applies each decision tree in the forest to that information, generating a number of estimated valuations. The facility determines an overall valuation for the distinguished home based on the valuations generated by the decision trees.

A method and system for limiting mobile device functionality when the mobile device is located in a pre-defined fixed geographic area. The mobile device may receive a local disabling signal from a disabling device, which, when detected by a Device Owner Application resident on the mobile device, limits the mobile device functionality. Alternatively, the system may use GPS based geofencing to determine that the mobile device is in a predefined geographic area, and then transmit instructions to the mobile device, where a Device Owner Application operates to restrict the mobile device functionality. Mobile device functionality may be limited by restricting the mobile device's ability to send and receive text messages, email messages and phone calls; restricting the mobile device's ability to respond to user input; restricting the mobile device's ability to utilize a web browser; and restricting the mobile device's web browser from accessing predetermined web sites.

Methods for training a series of neural networks to output driving behavior control parameters is disclosed. The training dataset for the neural networks includes sensor-based vehicle driving recordings that may be categorized by geographical area, by qualitative driving behaviors, or by some combination, such that various training data subsets are used to train the series of neural networks. By learning either city-specific driving behavior control parameters, qualitative driving behavior specific driving behavior control parameters, or both, the resulting parameters may then be provided to a motion planning model for use in modeling predictive control for an autonomous vehicle. Rather than relying on XYZ trajectories of agent vehicles when planning future trajectories of the ego vehicle, the motion planning model is adaptive, due to the use of the learned driving behavior control parameters.

Systems and methods are provided to select a set of candidate listings having geographic locations within a geographical area, fit a bound for a maps viewport to the set of candidate listings, generate a maximum score for the set of candidate listings based on a booking probability for each listing, a distance of the listing location from a center of the bound for the maps viewport, and visibility based on other listings. The systems and methods further optimize the listings shown in the maps viewport by removing and replacing farthest listings up to a predefined number of listings to be removed and computing a cumulative booking score for each set of listings to compare to the maximum score. The systems and methods cause display in a maps viewport in a user interface of a computing device of a set of listings that has a maximum score.

According to a communication method and related devices, first information received by a first communication device is used for indicating information of N satellite beams, and the first communication device can determine a target satellite beam communicating with a first terminal device based on the information of the N satellite beams. Wherein in each of the K time periods, a signal coverage area of at least one satellite beam in the N satellite beams comprises a first geographic area, and when the first communication device receives the first information, the terminal device located in the first geographic area comprises a first terminal device. In other words, any terminal device located in the first geographic area can determine the target satellite beam based on the information of the N satellite beams indicated by the first information. In this way, one or more terminal devices located in the same geographic area can determine the target satellite beam based on the first information, and the transmission overhead can be reduced.

A multi-static synthetic aperture radar using beamformed illumination beams and multiple collection satellites is described. An illumination satellite may be in first orbit and multiple collection satellites may be in a second orbit. The illumination satellite may transmit beam signals (e.g., communication signals carrying modulated data to user terminals) from an antenna array to different beam coverage areas according to a beamforming matrix. Each of the collection satellites may receive reflections of the beam signals. The reflected signals received at the collection satellites may be processed according to the beam signals and beamforming matrix used to transmit the beam signals to obtain an image of a geographical area. In some cases, the collection satellites may relay the received signals for processing via the illumination satellite.

The invention provides a business travel data management method and system based on cloud computing and scene matching and computing equipment. The method comprises the following steps: acquiring at least one geographic image uploaded by a user in a business travel plan execution process; generating a compliance geographic region set according to the travel plan; matching the geographic image with the compliance geographic region set to obtain a first matching result; identifying and extracting visual scene features in the geographic image, and performing consistency cross validation comparison on the visual scene features and a geographic scene expected based on geographic coordinates to obtain a first comparison result; generating an authenticity verification result of the travel behavior based on the first matching result and the first comparison result; and when the authenticity verification result is that the verification is passed, automatically generating an electronic audit voucher containing a spatial trajectory chain and a geographic image. According to the technical scheme of the invention, the accuracy of authenticity verification of travel behaviors can be improved, a high-credibility electronic audit voucher is automatically generated, and a data closed loop is provided for intelligent travel management.

The invention relates to the technical field of medical products, in particular to a medical product market prediction and supply chain collaborative management system, which comprises a health risk identification module, a risk trend analysis module, a demand sorting and grading module, a supply chain matching module and a distribution path optimization module. According to the method, through synchronous analysis of spatial data, the risk pattern of population mobility and disease distribution in each geographic area can be dynamically displayed, multi-dimensional accurate identification for high-risk areas of the mobility population is realized, fluctuation of market demands and potential public health pressure are actively identified based on a trend linkage triggering mechanism and in combination with a periodic change index, and the risk of population mobility and disease distribution in each geographic area is accurately identified. The early warning response speed is improved, multiple parameters such as drug use demand urgency levels, drug delivery timeliness and spatial distances are comprehensively considered, a demand sorting and resource allocation global system is constructed, supply and demand connection is promoted to be more scientific and reasonable, resource flows between medical institutions and storage nodes are matched, and multi-point inventory and priority demands are combined, so that the efficiency of early warning is improved. And the overall resource utilization rate is improved.

In some embodiments, a method comprises obtaining a video stream of a portion of a geographic area, the video stream comprising a plurality of video frames, each of the plurality of video frames captured at a respective first time. Contextual metadata is obtained, the contextual metadata associated with one or more objects located in the portion of the geographic area at a second time, the second time being before each of the respective first times. The contextual metadata is inserted into one or more of the plurality of video frames, thereby causing the contextual metadata associated with the one or more objects to be overlaid on one or more corresponding portions of the one or more of the plurality of video frames.

The present invention relates to the field of computer technology applications and specifically provides a parking stand association method, device, and medium based on hierarchical reinforcement learning, including: obtaining grid area information and associated subject information corresponding to a target geographic area; the grid area being the parking stand and the associated subject being the flight; based on the grid area information, dividing all grid areas corresponding to the grid area information into multiple mutually exclusive area groups; based on preset association constraint information, obtaining the associated subjects allowed to be associated with each mutually exclusive area group, and obtaining an initial association feasibility matrix for each mutually exclusive area group; based on the initial association feasibility matrix and preset mutual exclusion constraint information for each mutually exclusive area group, obtaining an associated operation heterogeneous graph for each mutually exclusive area group; and utilizing a hierarchical reinforcement learning model to obtain association results corresponding to each mutually exclusive area group. The present invention can improve parking stand allocation efficiency and docking rate.

Embodiments provide drought projection for real-world geographic areas. One such embodiment identifies at least one climate model associated with a real-world geographic area. An evaluation is performed to determine whether the identified at least one climate model is valid for direct drought projection for the real-world geographic area. Based on a result of the evaluating, a drought projection technique is selected and the drought projection technique selected is employed to generate at least one drought projection for the real-world geographic area. The at least one drought projection includes an indication of projected drought frequency, projected drought duration, and projected drought intensity.

The invention provides a mobile payment transaction behavior analysis method based on spatial data, and the method comprises the steps: obtaining time series data of transaction amounts in different geographic regions, carrying out the feature extraction, and forming a transaction amount fluctuation feature set; correcting a weight value of spatial-temporal heterogeneity according to the fluctuation period, the adjacent region sensitivity and spatial-temporal change characteristics of the inter-region transaction intensity, and determining an adaptive spatial weight matrix; acquiring a historical distribution mode of the potential abnormal region set transaction data, analyzing a deviation degree between the current fluctuation amplitude and the historical mode, judging the authenticity of an abnormal signal, and determining an abnormal event list; and calculating the influence diffusion degree of the abnormal events on the surrounding area to obtain the variance expansion degree, adjusting the abnormal detection parameters according to the abnormal event list, optimizing the detection rule, and determining the spatial dependency relationship network.

A power equipment machine with operator selectable autonomous mode and manual mode is provided. By way of example, the power equipment device can provide user-assisted semi-autonomous steering along user-defined paths of a geographic area. Manual steering controls and autonomous guidance controls can be positioned on one or more movable armrests to enhance comfort and minimize operator fatigue. The manual steering controls can include a jog wheel and encoder mechanism located on a first armrest, eliminating conventional mechanical shaft and wheel steering devices and dual lap bar steering devices. Guidance and computer settings can be accessed through a touchscreen display mounted in front of an operator position on the power equipment device.

The invention discloses a convective cloud synchronous monitoring method and device, a program product and a medium, and relates to the technical field of meteorological monitoring. The method comprises the following steps: acquiring reflectivity puzzle data; performing single-frame detection processing on the reflectivity puzzle data to obtain a convective cloud instance set; performing polygon overlap matching processing on the streaming cloud instance set to obtain a first matching pair set, a first unmatched instance set and a second unmatched instance set; performing nearest neighbor matching processing on the first unmatched instance set and the second unmatched instance set to obtain a second matching pair set; performing multi-mapping conflict detection processing on the second matching pair set to obtain a third matching pair set; combining the first matching pair set and the third matching pair set to obtain a target matching result set; and based on the target matching result set, updating time sequence state information of the convective cloud in the to-be-monitored geographic area. And the monitoring accuracy of the time sequence state information of the flow cloud is improved.

The invention provides a communication method, device and system, which can be applied to an NTN communication scene. The method comprises: sending first indication information to a first terminal device, the first indication information indicating a first parameter, the first parameter being used to configure a first mode, the first mode being a cell discontinuous reception (DRX) and / or discontinuous transmission (DTX) mode, the first parameter being a parameter of beam granularity, the first parameter corresponding to a first beam, and the first terminal device being used to configure a second mode, the first mode being a cell discontinuous reception (DRX) and / or discontinuous transmission (DTX) mode; the first beam belongs to at least one beam used for communication in the service cell; or, the first parameter is a parameter of the regional granularity, the first parameter corresponds to the first region, and the first region is one of a plurality of geographic regions covered by the serving cell of the first terminal device. According to the method, the application range and the use mode of the DTX / DRX mechanism are flexibly controlled, for example, different DTX modes (such as activation duration and period) can be configured for different geographic areas, so that the network equipment can enter a turn-off state (inactive state) and lasts for a period of time, the effect of network energy conservation is locally realized in a cell, and the network efficiency is improved. The dynamic power distribution effect in the cell is achieved, and the downlink coverage and capacity of the cell are improved.

The invention discloses a termite distribution inference model-based distribution early warning method, and relates to the technical field of termite prevention and ecological monitoring, and the method comprises the following steps: obtaining multi-source data associated with a target geographic area, the multi-source data comprising historical termite activity records, environmental factor data and engineering structure parameters; and according to a preset termite diet group classification system, assigning the termite samples in the historical termite activity records to one or more specific diet groups, and generating a sub-group activity data set. According to the method, termites are classified according to diet groups, and the exclusive prediction model is constructed, so that the problem of insufficient prediction precision caused by neglecting environmental response differences among groups in the prior art is solved. The method can accurately capture the specific association between different groups and environmental factors, and the generated sub-group distribution probability graph can clearly display the spatial distribution state of each group, thereby avoiding the overlapping of distribution characteristics.

Techniques for generating a capacity recommendation engine are described herein. First information that identifies capacity changes associated with shipping packages from one or more entities may be obtained. Each capacity change may be associated with a reason code that identifies a reason for the capacity change. Second information that identifies uncontrollable event data for one or more geographic areas that correspond to a delivery area for the one or more entities may be received. Historical information that includes historical capacity changes and associated reason codes from the one or more entities may be obtained. A machine learning algorithm may be implemented based on the first information, the second information, and the historical information. A recommendation may be generated for an entity using the machine learning algorithm. The recommendation may include a modification of a component associated with the shipping network of the entity for a future time period.

The invention discloses a meteorological disaster early warning response method and system based on artificial intelligence. According to the method, feature extraction processing is carried out on to-be-early-warned meteorological data of a target geographic area and meteorological source associated data of the to-be-early-warned meteorological data to obtain a meteorological multi-dimensional feature matrix; performing feature extraction on the at least two kinds of disaster associated data of the to-be-early-warned meteorological data to obtain at least one disaster representation vector of the to-be-early-warned meteorological data; performing fusion processing on the meteorological multi-dimensional feature matrix and the at least one disaster representation vector to obtain a fusion feature matrix; adopting a meteorological disaster model to predict the meteorological disaster type of the meteorological data to be pre-warned based on the fusion feature matrix; the corresponding early warning response service is triggered according to the predicted disaster grade and the scene type of the target geographic area, the meteorological disaster early warning response can be realized by adopting an artificial intelligence technology, the accuracy of the meteorological disaster early warning response is improved, and personalized early warning response services can be customized for different scenes.

An improved parcel growth prediction system that uses parcel data, population data, and artificial intelligence to predict the growth of a geographic area at a micro level (e.g., a real estate parcel level) is described herein. For example, the improved parcel growth prediction system may generate a graph model and apply the graph model as an input to an artificial intelligence model to predict the likelihood that a particular parcel may be developed some time in the future. Ultimately, implementing the improved parcel growth prediction system described herein may lead to more precise placements of infrastructure projects and / or to infrastructure projects that more precisely support the needs of the population of a geographic area as time passes.

A computerized method estimates crop growth in a geographic area using satellite-collected synthetic aperture radar (SAR) data. SAR data of the geographic area is obtained from a plurality of satellite passes by one or more satellites. The obtained SAR data is processed into coherence data and interferometric data. The processed data is associated with a comparison between a first SAR data subset from a first satellite pass of the plurality of satellites passes and a second SAR data subset from a second satellite pass of the plurality of satellite passes. The processed data is provided to a trained crop growth estimation model and a crop growth prediction associated with the geographic area is generated using the trained crop growth estimation model. In some examples, the obtained SAR data is processed into additional data types, such as amplitude data and / or polarimetric SAR data.

One example operation includes one or more of the following: determining, by a first node, that an event has occurred in a geographic area; predicting, by the first node, a severity of the event, a duration of the event, and at least one vehicle associated with the event; sending, by the first node to a second node, the prediction and a time at which the at least one vehicle will approach the second node; and before the time, sending, by the second node, a notification to other vehicles proximate to the second node to maneuver based on the prediction.

A system and processes detect, identify a Unexploded Ordnances (UXOs) and / or categorize UXOs in near real-time using Unmanned Aerial Vehicles (UAVs). In accordance with various disclosed embodiments, the equipment includes such UAVs (also referred to herein as “drones,”) that include a plurality of sensors for imaging the terrain of a geographic area to analyze the terrain and detect anomalies and / or changes that may be indicative location of UXOs, for example, soil moving activity performed in association with the burying the UXO. Additionally, the UAVs include processing equipment, e.g., one or more small form factor devices, e.g., Next Unit of Computing (NUC) compute elements or the like, that provide processing power to provide EDGE computing on the data gathered at the UAV.

Some embodiments of the present disclosure provide inventive concepts for estimating a value for an environmental heat stress index at a target microscale location. Meteorological data indicative of parameters such as relative humidity, air temperature, wind characteristics, or atmospheric cloud cover for a geographic area representative of a larger geographic area that includes the target microscale location can be obtained. Localized terrain data specific to the target microscale location can be obtained. A bias correction can be performed on the meteorological data based on the localized terrain data, generating microscale meteorological data that reflects conditions at the target microscale location. The heat stress index value, representing heat-related risk specific to the target microscale location, can be determined using the microscale meteorological data and can be a real-time or forecast value used for providing actionable insights or alerts for health and safety purposes.