460 results about "Geographic regions" patented technology

A drive unit adapted for use in farm equipment includes a first transceiver and a processing module. The processing module is configured to receive first data from the farm equipment, including sensor data and / or controller data, and integrate the received first data with additional second data indicating an agricultural prescription for a geographic region to form agricultural data. The processing module is also configured to apply one or more spatial filter constraints and temporal filter constraints to the agricultural data and then compute, from the filtered agricultural data, a difference between actual execution of one or more parameters of the agricultural prescription and expected execution of the one or more parameters. The processing module is further configured to, when the difference exceeds a predefined stored threshold, autonomously generate and transmit to a remote computing device, a settings alert to thereby trigger generation of an updated agricultural prescription.

A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.

The invention provides a grape disease and pest identification method based on an LW-YOLO network, and aims to improve small target detection precision and multi-scale feature extraction capability and reduce model calculation complexity at the same time. The method comprises the following steps: step 1, acquiring a public grape disease and insect pest data set or establishing a data set according to requirements of a specific grape variety, a specific geographic area and a special disease and insect pest variety; step 2, manually marking a category label and a bounding box of each string of grapes in each picture in the grape disease and insect pest enhancement data set; 3, configuring a model training environment; the method comprises the following steps: step 1, constructing an LW-YOLO network model, step 2, constructing an LW-YOLO model, step 5, loading the constructed LW-YOLO network model to a configured model training environment, and training and verifying the LW-YOLO network model by using a preprocessed data set; and step 6, constructing a grape disease and insect pest recognition system based on an LW-YOLO network, splitting the system into a visualization subsystem and a disease and insect pest analysis subsystem, enabling the visualization subsystem to realize a real-time video stream lightweight image interception function by using an FFmpeg library in a Javacv library, and providing pictures to the disease and insect pest analysis subsystem for disease and insect pest target detection. And finally, displaying a detection result to a front-end page.

A system and apparatus for selectively limiting user control of a mobile electronic device are provided. In an embodiment, the apparatus may be a case sized to receive a mobile electronic device, the case having a locking means to at least partially secure an opening thereof. When engaged, the locking means may be physically impenetrable by the user of the mobile electronic device and may further be configured to engage and disengage in the presence of one or more pre-determined conditions. For example, the lock may engage when the case is detected within a certain geographical region or even during a specified time. It is contemplated that providing disclosed systems and apparatuses may reduce undesirable behavior at communal events by eliminating distractions posed by mobile electronic devices and preventing unauthorized recordings, which together may enhance overall engagement at and enjoyment of such events.

The invention relates to the field of smart cities, in particular to an urban landscape semantic segmentation method and system based on a large model, and the method comprises the steps: obtaining street view image data of a target geographic region; inputting the streetscape image data into a preset image semantic segmentation model, analyzing the streetscape image data through the image semantic segmentation model, and identifying various predefined environmental element categories contained in each image; calculating an urban landscape quantitative index based on the recognition result of the environmental element category; associating the calculated value of the urban landscape quantitative index to a corresponding road section or node in the road network spatial data corresponding to the target geographic area; generating a road network space visualization layer representing the value of the index value in a color grading manner based on the result after association; and according to a preset index threshold value, identifying the spatial road sections or nodes meeting the early warning conditions in the visual layer and generating prompt information. And the scientificity and comparability of an analysis result are ensured.

The invention discloses an intelligent crop recommendation system based on a neural network, and the system comprises the following modules: an agricultural multi-source data acquisition module which collects and preprocesses agricultural data of different geographic regions and seasons; the cross-environment data division module is used for forming a plurality of independent environment data sets; the deep feature extraction network module is used for extracting high-dimensional semantic features; the feature invariance constraint optimization module is used for constructing a unified stable feature model meeting cross-regional cross-seasonal generalization requirements; the generalization performance verification module is used for quantitatively evaluating model generalization accuracy; the personalized crop recommendation decision module is used for generating a personalized crop recommendation list; and the recommendation result visual interaction module is used for displaying the recommendation result to the user. According to the method, the cross-environment generalization ability and the personalized recommendation accuracy of the crop recommendation system are remarkably improved.

The invention relates to the technical field of mineral exploration, and discloses a method for estimating the mineralization probability of residual slope product type anatase based on multi-source parameters, and the method comprises the steps: determining a geographic region, collecting geological data, sampling a geological sample, and collecting the characteristics of the sample; extracting the first characteristic index set to judge the region classification of the geographic region, wherein the region classification comprises a conventional exploration region, a main mineralization mode region and a secondary mineralization mode region; extracting a second characteristic index set from the geological data to calculate a standard mineralization probability; calculating a weathering correction coefficient and a maturity correction coefficient; and correcting the standard mineralization probability to generate a mineralization probability, and judging whether the geographic region is a high-potential target region or not according to the mineralization probability. According to the technical scheme, the metallogenic probability judgment standard can be determined and corrected under different conditions, and judgment of a high-potential target region is achieved.

A method includes determining that a user device is located within a specified geographical region and determining that the user device has an active subscription with a terrestrial network operator operating a terrestrial network within the geographical region, the terrestrial network associated with a primary profile stored on a subscriber identity module of the user device. The method also includes, in response to determining that the user device is located within the geographical region and that the user device has an active subscription with the terrestrial network operator, receiving, from the terrestrial network operator, a secondary profile attached to the active subscription and associated with a non-terrestrial communication network. The method further includes storing the secondary profile on an embedded subscriber identity module on the user device and communicating via the non-terrestrial communication network using the secondary profile.

The invention belongs to the technical field of product production cycle management, and particularly relates to an electronic image-text file remote multi-bin collaborative storage method based on a PLM. Comprising the steps that a 1 + N hybrid storage architecture is constructed, one center bin is deployed in an enterprise headquarter data center and stores full document metadata and latest version files, and N area bins are deployed according to geographic areas and store cache copies and incremental update data of local high-frequency access documents; the remote multi-warehouse document increment synchronization is realized through an intelligent synchronization engine, and the intelligent synchronization engine adopts a content awareness increment synchronization algorithm and a dynamic synchronization strategy based on network quality; version management is realized through a version cooperative control module, and the version cooperative control module is used for establishing a global version number mapping mechanism and a conflict resolution decision tree; cross-warehouse process automation is realized through a cross-warehouse workflow engine, and the cross-warehouse workflow engine integrates a BPM process engine and a message queue; the method can adapt to a remote network environment, and the large file synchronization efficiency is improved.

Embodiments of the present disclosure relate to migrating data from the cloud to on-premises storage. More specifically, a processing device obtains an indication of a plurality of cloud storage resources associated with a plurality of cloud storage providers and a plurality of geographic regions. The processing device generates an on-premises file structure based on the indication of the plurality of cloud storage resources and the plurality of geographic regions. The processing device migrates the plurality of cloud storage resources to on-premises storage based on the on-premises file structure.

Techniques are disclosed relating to partitioning batch queries for multi-engine execution. A system receives a batch query specifying query data and including a request for geospatial data for regions corresponding to the query data. Based on locations corresponding to the query data, the system partitions the query data into subsets. The system may assign the subsets of query data to query engines corresponding to the locations of the subsets. The system may cause the engines to retrieve geographic region data corresponding to the locations included in the subsets, where the retrieving is performed by a given query engine for a corresponding subset by accessing an in-memory index of the given engine that stores geographic region data for a geographic partition within which the corresponding subset of query data is located. The system may store region data retrieved by the engines for the subsets in an aggregated data store.

A communication system for inventory management via wireless monitoring comprises radio frequency identification (RFID) tags configured to operate in a frequency band that is unlicensed in a first geographic region and is licensed in a second geographic region. The communication system also comprises a RFID reader designed according to operational parameters of the first geographic region. The RFID reader is configured to read, in the second geographic region, data from the RFID tags via radio frequency (RF) signal transmissions in the frequency band at a power level that exceeds the operational parameters of the first geographic region. The RFID reader comprises a RF circuit, an antenna, and a controller. The controller is configured to program the RF circuit with second operational parameters, which cause the RFID reader to operate outside of requirements for operation in the unlicensed frequency spectrum in the first geographic region as defined by the operational parameters.

The invention relates to the technical field of remote sensing intelligent interpretation, in particular to a multi-modal remote sensing change detection method and system based on multi-granularity alignment and frequency adaptive fusion, and the method comprises the steps: obtaining optical remote sensing images and SAR remote sensing images of the same geographic region and different time phases, and constructing a multi-modal image data pair; respectively extracting multi-scale features of the optical remote sensing image and the SAR remote sensing image by adopting non-weight sharing double encoders; a multi-modal feature alignment module is used for carrying out feature distribution alignment on multi-scale optical features and SAR features on different spatial scales through KL divergence constraint, in the alignment process, a change area is eliminated through a binary change mask, and alignment constraint is only carried out on an unchanged area; constructing a unified cross-modal embedding space by comparing and learning related semantic alignment modules; and designing a modal coupling attention mechanism, and performing frequency adaptive fusion on the multi-modal features in a frequency domain. According to the method, accurate cross-modal alignment and complementary feature fusion are realized, and the accuracy and reliability of change detection are remarkably improved.

A method, apparatus, and computer program product are provided for establishing and applying a second level of anonymization of trajectories to improve privacy. Methods may include: receiving anonymized mobility data for a geographic region subdivided into a plurality of map tiles, where the mobility data includes a plurality of probe data points anonymized with a first anonymization level, where sequences of probe data points define trajectories; determining, based on the anonymized mobility data, one or more map tiles of the plurality of map tiles where anonymization of trajectories using the first anonymization level fails to satisfy a predefined anonymity parameter value; removing a subset of the anonymized mobility data corresponding to the one or more map tiles; and publishing the anonymized mobility data without the subset of the mobility data corresponding to the one or more map tiles for use with one or more location-based services.

A method of predicting errors in location sensor data is disclosed. The method may include retrieving, from a map database, lane geometry information associated with a first lane within a first road link in a geographic region and obtaining the first location sensor data for a first set of reference points. The method may further include associating each reference point with a respective portion of the lane geometry information and calculating a first error associated with the obtained first location sensor data based on the respective portion of lane geometry information. The method may further include obtaining a first set of features associated with each of the first set of reference points and training a machine learning (ML) model using the calculated first error and the obtained first set of features.

The invention discloses an extreme high temperature inter-year prediction method and device and a storage medium, and belongs to the technical field of inter-year prediction, and the method comprises the steps: obtaining a second extreme high temperature strength index extracted based on multi-mode multi-initial-field return test data in a target prediction time period, inputting the second extreme high temperature strength index into a pre-trained Transform-based region prediction model, and obtaining a prediction result of the second extreme high temperature strength index; acquiring an extreme high-temperature strength inter-year prediction sequence of each region in the target geographic region; and reconstructing the extreme high-temperature strength inter-year prediction sequence of each region to the spatial grid points through a linear projection method to obtain a spatial distribution prediction result of the extreme high-temperature strength in the target geographic region. According to the method, the capability of a power mode for summer extreme high temperature strength interannual prediction is remarkably improved, and the defects of an existing mode in extreme high temperature strength interannual prediction are effectively overcome.

The invention discloses an adaptive feature alignment crop identification method based on intelligent remote sensing interpretation, and the method comprises the steps: taking DINOv3 as a backbone network, and optimizing the backbone network through a feature normalization adaptation layer and a resolution adaptive module; a time sequence feature alignment module TAFA is adopted to carry out cross-time alignment on crop features of remote sensing images in different growth periods; a geographic context gating attention module GCAI is adopted, and cross-geographic region adaptive feature fusion is realized based on multi-scale geographic context coding and a CNN-Transform bidirectional interaction mechanism; a crop semantic contrast loss function CSCL is adopted to perform intra-class compactness and inter-class separation degree contrast optimization on features of crops of the same class. The method realizes cross-growth-period stability and cross-region adaptation under a pure supervised training normal form, can distinguish remote sensing crop high-precision semantic segmentation of similar crops, covers agricultural remote sensing image processing scenes of different geographical landforms and whole growth periods of crops, and can be applied to the fields of modern agricultural fine management and the like.

Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.

The invention discloses a social relation chain value evaluation method and system based on multi-dimensional data modeling, and the method comprises the steps: integrating a social platform API, a user behavior log, geographic position data and equipment information through a data collection module, carrying out the cleaning and standardization of a preprocessing module, dividing a geographic region through a space-time modeling module based on K-means clustering, and carrying out the calculation of the value of a social relation chain. An exponential decay function is introduced to dynamically update an interaction weight, an emotion analysis module adopts a BERT model to carry out emotion classification on comments, a role classification module marks community roles through a rule engine, and finally a value calculation module fuses feature parameters of data of all dimensions to construct a propagation value evaluation model. According to the method and the system, the propagation value evaluation accuracy is remarkably improved, the misjudgment rate is greatly reduced, the social e-commerce propagator screening efficiency, the local life service precise popularization coverage rate and the brand propagation positive word-of-mouth recognition accuracy are improved, and scientific decision support is provided for social fission marketing.

Embodiments herein provide various apparatuses and techniques that indicate multiple power classes that user equipment may support to a network. The user equipment may support at least a lower power class (e.g., having a lower maximum transmission power) and a higher power class (e.g., having a higher maximum transmission power) to the network. The user equipment may send indications to the network that it supports both the lower power class and the higher power class. If the network / user equipment are in a geographical region associated with regulations limiting transmission power to the lower maximum transmission power, then the network may configure a transmission power characteristic based on the lower power class. If the network / user equipment are in a geographical region associated with regulations limiting transmission power to the higher maximum transmission power, then the network may configure the transmission power characteristic based on the higher power class.

Computer-implemented methods optimising routes in a transport network for a geographical region are disclosed. Methods comprise obtaining an analysis of the geographical region; generating probabilistic predictions, checking whether at least a subset of the generated probabilistic predictions have previously been solved by a trained machine learning model; based on at least a first subset of the generated probabilistic predictions having previously been solved, retrieving the trained machine learning model previously used to solve the at least a first subset of the generated probabilistic predictions and executing the trained machine learning model to determine a first plurality of suggested routes for the transport network; based on at least a second subset of the generated probabilistic predictions not having previously been solved, training a new machine learning model to solve the at least a second subset of generated probabilistic predictions to determine a second plurality of suggested routes for the transport network.

Implicit occupancy for autonomous systems include receiving a request for a point attribute at a query point matching a geographic location, obtaining a query point feature vector from a feature map. The feature map encodes a geographic region that includes the geographic location. A first set of multilayer perceptrons of a decoder model process the query point feature vector to generate offsets. Offset feature vectors are obtained from the feature map for the offsets. A second set of multilayer perceptrons of the decoder model process the offset feature vectors and the query point feature vector to generate the point attribute. The operations further includes responding to the request with the point attribute.

The invention relates to a method of encoding image data representing a geographical line, GL, or its surroundings, and a data processing system being configured to perform the encoding. The method comprises applying a data compression process to reduce the spatial resolution of a given image represented by the image data. Specifically, the image may form an imagery layer of an imagery stack comprising various imagery layers, each imagery layer representing an individual image, e.g. of a same geographical region. The data compression process comprises: generating two or more sublayers of the image by resampling at least one region of the image at a respective lower spatial resolution than its original spatial resolution, while maintaining or resampling the spatial resolution of at least one other region of the image such that, when the sublayers are overlapped to jointly define a combined multi-resolution representation of the image, the average resolution of the combined multi-resolution representation is lower than the original resolution of the image.

The invention provides a city image geographic positioning method based on visual clues and retrieval enhancement generation, and the method comprises the steps: decomposing a city image geographic position reasoning task into a multi-step reasoning stage based on a multi-stage reasoning process of image dominant information; the positioning range is gradually reduced through specific task division of each step and the input and output relation of the specific task division, and reasoning controllability and interpretability are enhanced; secondly, a thinking chain is designed according to reasoning requirements, a modular knowledge base is designed according to task knowledge requirements, high-accuracy geographic position reasoning is achieved in combination with a retrieval enhancement generation technology, and model interpretability is enhanced; finally, a modular knowledge base supporting cross-modal retrieval is constructed, knowledge base modules are divided according to different geographic areas, architectural styles, language culture and the like, and the efficiency and accuracy of knowledge retrieval are improved.

The invention relates to a federated learning-based privacy protection personalized recommendation system, which comprises a user side, an edge side and a cloud side, and is characterized in that the user side is a basic unit for data generation and local training, each user maintains a personalized recommendation model on own local equipment, the edge side is used as an intermediate aggregation layer, and the cloud side is used as a cloud side; the cloud end is used for preliminarily aggregating local models of users in a certain geographic area or logic area, the edge end is generally deployed in an edge server or an area data center, and the cloud end is a central node for updating and distributing a global model and is used for secondarily aggregating area aggregation models uploaded by the edge ends to form a global recommendation model; according to the scheme, user privacy is strictly protected, and collaborative recommendation is realized under the condition of not transmitting original user data through a differential privacy and security aggregation dual protection mechanism.

A constellation of satellites provides communication services to user terminals (UTs). Some UTs may be fixed while others are mobile. Each satellite provides communication services to discrete geographic areas or “spots” on and above the Earth. The mobile UT may operate in various modes. In a normal mode, communication resources within a single spot are allocated to a mobile UT. An obfuscated mode allocates the mobile UT communication resources in many spots, even though the mobile UT is only physically present in one. A whisper mode minimizes transmission from the mobile UT. With the exact location of the mobile UT unknown to the satellites, the mobile UT may send a handover request that is used to schedule a handover to other satellite(s), providing ongoing communication service while the mobile UT moves. In some implementations the handover request is determined in advance. A quiet mode facilitates the mobile UT receiving only.

The invention discloses an early cancer screening model based on a cloud architecture and a construction method, and particularly relates to the technical field of medical artificial intelligence. The method comprises the following steps: firstly, collecting and normalizing heterogeneous medical image and biomarker data of a plurality of geographic areas, and constructing a unified data set; then extracting image features and marker features by adopting multi-task learning, fusing equipment and region attributes, establishing a joint representation vector, and training a preliminary screening model; after the model is deployed to a cloud end, target area data is adapted through online transfer learning, and an area model is generated; further extracting an activation heat map, identifying a weak signal high response area in combination with sensitivity analysis, comparing with manual annotation, constructing a local weight adjustment strategy based on errors, optimizing the model, and outputting a final model; the method improves the recognition capability of early lesions under the condition of low signal-to-noise ratio, is suitable for remote and basic screening scenes, and has a high practical value.

The invention provides a meteorological data simulation method and system based on a generative adversarial network, and the method comprises the steps: firstly obtaining a historical meteorological data set containing a time sequence relation and geographic region identification information, inputting the historical meteorological data set into a time sequence rule learning model, and extracting a meteorological evolution rule feature set; the method comprises the following steps: constructing a meteorological evolution rule feature set which covers time sequence relevance features of meteorological elements and geographical area relevance features, then constructing a conditional generative adversarial network model which comprises a generator module and a discriminator module, inputting the meteorological evolution rule feature set into the generator module, generating a virtual meteorological field data set with geographical area identification information, the virtual meteorological field data set comprises meteorological element distribution information consistent with the spatial-temporal characteristics of the historical meteorological data set, and finally outputting the virtual meteorological field data set to simulate the meteorological scene evolution process of the specific geographic area, so that the meteorological scene evolution of the specific geographic area can be effectively simulated. And the accuracy and the practicability of meteorological simulation are remarkably 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.

Systems and methods are disclosed for determining at least one geographic region of a plurality of geographic regions, at least one data variable, and / or at least one health variable, estimating a current prevalence of a data variable in a geographic region of the plurality of geographic regions, determining a trend in a relationship between the data variable and the geographic region at a current time, determining a second trend in the relationship between the data variable and the geographic region at at least one prior point in time, determining if the trend in the relationship is irregular within a predetermined threshold with respect to the second trend from the at least one prior point in time, and, upon determining that the trend in the relationship is irregular within a predetermined threshold, generating an alert.