985 results about "Geolocation" patented technology

A decentralized event detection and geolocation system is disclosed, utilizing a dynamically adaptable Spontaneously Emergent Geolocation Network (SEGNet) comprising mobile, stationary, and other devices equipped with multi-sensor capabilities. The system operates by obtaining sensor data from distributed devices, applying trained models to detect potential events of interest (EOIs), and dynamically forming device networks for collaborative geolocation and event validation. The system employs synchronization mechanisms and geolocation techniques, including Time Difference of Arrival (TDOA) and / or Angle of Arrival (AoA), to determine event locations through multimodal data fusion and iterative validation processes. The architecture supports multiple operational modes, including decentralized, centralized, and hybrid configurations, enabling operation in connectivity-limited environments and enhanced processing when server access is available. The system is designed to accommodate dynamic task delegation and real-time threshold adjustments based on environmental conditions, providing reliable event detection and geolocation across diverse scenarios.

The present application relates to PPP-RTK, RTK, SSR, or like correction data based devices and methods to produce geolocation solutions with improved accuracy. Further, such devices and methods may be further employed in various related utility locator devices, utility locating transmitters, and other utility locating and mapping devices.

An object tracking device, such as may be attached to a trailer to track movements of the trailer, may have various communication capabilities, such as cellular / LTE, Wi-Fi and GPS, which each have varying power requirements. A power optimization routine determines which communications should be performed to conserve battery while also providing frequent location updates. The routine determines if another plugged in tracking device is within low power Bluetooth (BLE) range and, if so, communications location via that device. If not, a Wi-Fi scan is performed and a lookup table is consulted for the corresponding location of any identified Wi-Fi access points. If location data cannot be found for an identified Wi-Fi access point, GPS location of the device is determined and the lookup table is updated to include the GPS location of the Wi-Fi access point.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

A method for fetching a content from a web server to a client device is disclosed, using tunnel devices serving as intermediate devices. The tunnel device is selected based on an attribute, such as IP Geolocation. A tunnel bank server stores a list of available tunnels that may be used, associated with values of various attribute types. The tunnel devices initiate communication with the tunnel bank server, and stays connected to it, for allowing a communication session initiated by the tunnel bank server. Upon receiving a request from a client to a content and for specific attribute types and values, a tunnel is selected by the tunnel bank server, and is used as a tunnel for retrieving the required content from the web server, using standard protocol such as SOCKS, WebSocket or HTTP Proxy. The client only communicates with a super proxy server that manages the content fetching scheme.

A method for fetching a content from a web server to a client device is disclosed, using tunnel devices serving as intermediate devices. The tunnel device is selected based on an attribute, such as IP Geolocation. A tunnel bank server stores a list of available tunnels that may be used, associated with values of various attribute types. The tunnel devices initiate communication with the tunnel bank server, and stays connected to it, for allowing a communication session initiated by the tunnel bank server. Upon receiving a request from a client to a content and for specific attribute types and values, a tunnel is selected by the tunnel bank server, and is used as a tunnel for retrieving the required content from the web server, using standard protocol such as SOCKS, WebSocket or HTTP Proxy. The client only communicates with a super proxy server that manages the content fetching scheme.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

A method includes accessing a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; for each material of at least a subset of the plurality of materials in the material database: transmitting, via an RF transmitter, an RF signal into a target at a first resonance frequency for each material; receiving, via an RF receiver, a response signal from the target; and analyzing the response signal for resonance characteristics that indicate a presence of each material in the target; generating a report of each material in the target indicated by the resonance characteristics; and integrating the report with at least one of: a timestamp; image or video data from a camera; or geolocation data from a geolocation sensor.

Methods and systems for managing operations of data processing system are disclosed. To manage operations of the data processing system, a management controller of the data processing system may obtain and provide validated location data in the form of a location report for the data processing system to an external device via an out-of-band communication channel. The validated location data may be used to verify whether the data processing system is following expected geographical routes. By identifying any inconsistencies between the validated location data and expected geographical locations, the likelihood of identifying potential unauthorized modifications or other forms of compromise of the data processing system may be increased.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention provides an intelligent inspection and diagnosis system, method and device for a photovoltaic power station unmanned aerial vehicle and a medium, and the system comprises a heterogeneous perception fusion module which is used for constructing digital mirror image mapping of a power station global physical field through multi-mode sensor space-time coding and electromagnetic fingerprint matching; the group intelligent decision module is used for realizing autonomous task negotiation and anti-fragile path evolution of a multi-unmanned aerial vehicle cluster based on a dynamic Bayesian game model; the embedded diagnostic kernel module is used for running a quantization feature extraction algorithm at an edge computing node to realize component-level defect subsurface-level diagnostic reasoning; the superbody self-evolution module is used for continuously reconstructing a diagnostic knowledge graph by means of a genetic programming framework to realize Darwin asymptotic optimization of a system cognitive architecture; the problems that in unmanned aerial vehicle inspection, fault type judgment and accurate geographic positioning cannot be conducted online, photovoltaic array arrangement changes and sudden shielding scenes cannot be self-adapted, and transmission bandwidth limitation and analysis lag are likely to happen in a 5G weak coverage area are solved.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

A computing device for generating on-demand products based upon real-time geolocation data is configured to receive and store at least one product definition associated with a respective product. Each product definition defines an availability of the product and includes a product availability location. The computing device is also configured to receive real-time location data from a user computing device, and periodically query a memory device with the real-time location data. The computing device detects that the user computing device has entered a first location by retrieving, in response to a successful query of the memory device, a first product definition associated with a first product having a product availability location including the first location. The computing device is also configured to generate a product identification message including at least a portion of the first product definition, and transmit the product identification message to the user computing device.

The invention relates to the technical field of cross-view image positioning, in particular to a cross-view image geographic positioning method and system for an unmanned aerial vehicle and a satellite image, and the method comprises the steps: employing a Dinov2 large model after Conv-LoRA fine adjustment as a feature encoder, and extracting an unmanned aerial vehicle image feature vector and a satellite image feature vector; designing a spatial relation perception feature aggregator MSRA based on a Mama module for aggregating image features, and embedding spatial configuration features into a global descriptor; an InfoNCE loss function is adopted to train the model; and after feature vectors of a to-be-queried image and each image in the reference image library are extracted by using the trained model, a similarity score is calculated by using cosine similarity, and the unmanned aerial vehicle-satellite image pair with a high matching degree is selected to realize geographic positioning of the to-be-queried image. According to the method, Conv-LoRA is adopted to carry out fine tuning on a Dinov2 large model, so that the quantity of parameters needing to be trained is greatly reduced; a spatial configuration relationship between features is fully mined through a spatial relationship perception feature aggregator based on a Mama module.

Embodiments include a system for material detection and identification. The system may include an RF transmit device configured to transmit into an environment an RF signal at a resonance frequency. The system may also include an RF receiver device configured to receive a response signal from the environment. The system may further include a communication device connecting the RF transmit device and the RF receiver device. The communication device may include a control module. The system may include a geolocation device. The control module may be configured to receive geolocation data from the geolocation device, analyze the response signal for resonance characteristics that indicate a presence of a material, and identify the material to a user when the presence of the material is indicated by the resonance characteristics. Methods for material detection and identification are also described. Methods may involve obtaining geolocation data.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention relates to the technical field of distributed computing and computing power scheduling, in particular to a computing power resource fusion method based on a distributed flow pipeline, which comprises the following steps of: disassembling a user computing power request into a flow pipeline unit for packaging computing logic, an input / output interface and a resource demand label; meanwhile, computing power types, real-time load rates, memory occupancy rates, network round-trip delays, geographic positioning and energy consumption data of cloud edge end nodes are collected, a hierarchical topology network framework is constructed based on the collected data, node computing power available values are calculated, an inter-node transmission cost matrix is generated, a fault probability prediction model is constructed, and a fault probability prediction model is constructed. And finally, analyzing a data dependency relationship of the pipeline unit through a four-dimensional joint decision engine, executing dynamic mapping, and preferentially mapping the high-computing-power demand unit to a GPU cluster node, so as to realize non-interruption reconstruction during pipeline topology operation. And the global computing power resource utilization rate, the task operation efficiency and the service stability are improved.

The invention provides a road asset patrol method based on machine vision and related equipment thereof, which are used for improving the digital management level of road asset patrol. The method comprises the following steps: performing multi-source data dynamic synchronous acquisition on a road area to obtain an original road image matrix and corresponding shooting positioning data, and performing adaptive filtering and geometric correction on the original road image matrix to generate an enhanced image tensor, performing space-frequency spectrum joint feature extraction on the enhanced image tensor to obtain an asset feature vector set, performing multi-task classification on the asset feature vector set to obtain an asset category-position tensor, and performing tight coupling geographic positioning mapping on the asset category-position tensor according to the shooting positioning data to generate a geographic coordinate asset list. And performing spatio-temporal topology analysis on the geographic coordinate asset list to obtain a road asset map. And performing time sequence forward evolution prediction according to the road asset map to obtain an asset degradation prediction curve.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

A portable device for agricultural applications, consisting of: a microcontroller-based processing unit (101); a plurality of integrated environmental sensors (102), including temperature, humidity and barometer sensors; wireless communication modules (103) supporting Bluetooth, Wi-Fi and LoRa protocols; a GPS module (104) for geolocation; a display interface (105) with touch and haptic feedback; wherein the device is configured to communicate with nearby edge computing nodes to receive localized agricultural predictions based on real-time environmental data.

Geo-localization is the process by which an entity, such as an autonomous vehicle, can determine its precise location. In an example, a vehicle may include sensors, such as 4D radar sensors, that can capture a point cloud representing an environment within which the vehicle is positioned. An efficient representation of the point cloud is generated, which may be preceded by filtering the point cloud by removing points associated with transient elements. The efficient representation may be based on applying dimensionality reduction techniques to the point cloud or the filtered point cloud. The efficient representation may be compared with previously-generated representations which include geo-location, and were generated by the same dimensionality reduction techniques. Based on determining a match, the geo-location associated with the matched previously-generated representation may be assigned as a current geo-location of the vehicle.

The invention provides a dead wood quantity counting and positioning method based on an unmanned aerial vehicle ortho-video. The method comprises the following steps: constructing a data set according to an original video frame of the unmanned aerial vehicle ortho-video and synchronous positioning data; a deep learning model based on a single-stage detection architecture is trained, target detection is performed on frame-by-frame images, basic tracking is realized by detecting an associated multi-target tracker, periodic enhanced detection is performed in combination with a slice detection strategy, a redundant detection frame is eliminated through an overlapping region fusion algorithm, and a high-confidence target detection result is formed; based on a target motion track and space-time continuity analysis, appearance-tracking-loss-recovery period management of a dead wood target is realized; based on the mapping relation between the pixel coordinate system and the geographic coordinate system, the target position is converted into a GPS coordinate in combination with the flight parameters of the unmanned aerial vehicle, and a dead wood statistical result containing the space-time distribution characteristics and geographic positioning information are output.

Systems and methods are disclosed for executing large quantum programs efficiently and securely. It breaks down programs into small, logical components, leveraging runtime analysis and AI-driven labeling. These components are then deployed across distributed quantum hardware nodes based on factors like noise levels, capabilities, and geolocation, optimized by a deep learning engine. Ownership and deployment paths are tracked using non-fungible tokens (NFTs) on a blockchain network, ensuring security and transparency. Outputs from each node are validated and aggregated based on their NFT linkage, resulting in accurate and reliable program results. This distributed quantum DevOps framework promotes scalability, performance optimization, and secure management, accelerating the development and application of quantum computing across diverse fields.

The invention discloses a video geographic positioning method and device based on iron tower monitoring, and the method comprises the steps: constructing a monitoring video homonymy point sample library, collecting the homonymy point data of a monitoring video and a remote sensing orthoimage, and storing a reference image; establishing an initial calibration mapping relation, and realizing coordinate transformation of the remote sensing image and the video image through projection transformation; matching a reference image closest to the real-time video frame from a reference image library through image retrieval; dense matching of inclined images is carried out by using a deep learning model, and automatic alignment of feature points is realized; and resolving a video image coordinate to a geographic coordinate through homography transformation and an initial calibration model to complete high-precision positioning. According to the method, the bidirectional conversion function of finding the land by the video and finding the video by the land is supported, the application efficiency of iron tower monitoring in the fields of cultivated land protection and the like is effectively improved, a good positioning effect is still achieved for complex scenes such as plains, mountainous regions and the like, and corresponding technical support is provided for wide application of iron tower video monitoring.

The invention relates to the technical field of radar remote sensing, discloses a high-order loose body historical activity detection method based on multi-source DEM data, and aims to solve the problem that the historical activity of a high-order loose body in a glacier coverage area is difficult to accurately and quantitatively evaluate in the prior art. The scheme mainly comprises the steps that drainage basin units are automatically divided based on DEM data, and glacier influence areas are screened; multi-source DEM data are fused, and system errors are eliminated through geographic positioning deviation correction, elevation distortion deviation correction and track mode deviation correction; establishing a penetration depth physical model, and correcting the radar penetration depth in the glacier area by using the C / X wave band elevation difference optimization parameter; calculating an elevation change rate by adopting robust regression; and calculating an activity comprehensive index by integrating multiple factors, and identifying a hot spot region and a time period through probability estimation and spatio-temporal clustering. According to the method, automatic and high-precision quantitative evaluation on the historical activity of the high-position loose body is realized, and the method is particularly suitable for geological disaster early warning and risk evaluation in high mountain areas.

A system for adaptive multi-factor authentication using NFC tags in identity management networks, consisting of: an NFC-enabled authentication device configured as a passive label embedded in a smart card, a wearable device, or a physical token, the device including a secure element for storing a cryptographic key pair and a unique device identifier; a terminal device comprising an NFC reader module, a cryptographic coprocessor, a biometric capture interface, and a local control processor configured to initiate a cryptographic challenge-response authentication protocol with the NFC-enabled authentication device upon proximity detection within 10 centimeters; a backend identity management server connected to the end device via a secure communications network, the server comprising a context-aware policy engine, a machine learning-based risk assessment module, and an identity federation interface; wherein, upon receiving a response to a cryptographic challenge signed with the private key embedded in the NFC device, the end device transmits this signed response, biometric data, and context-aware metadata including a timestamp, geolocation of the end device, and device trust level to the backend identity management server; and wherein the backend identity management server evaluates a dynamically generated risk assessment based on real-time behavior analysis, authentication history, and context data and applies an adaptive authentication policy that conditionally permits access;denied or escalated to additional authentication factors.

Techniques are disclosed for hybrid machine learning model training and deployment to mobile edge devices. An example system includes a processor communicatively coupled to a memory and configured to execute instructions. Example instructions include receiving geolocation data from mobile edge devices; using the geolocation data to index the mobile edge devices into sub-regions; ranking associated machine learning (ML) models in the sub-regions based on performance metrics; and using a current geolocation of a particular mobile edge device within a given sub-region to serve a selected ML model to the particular device, wherein the ML model to serve is selected based on the performance metrics of the selected ML model within the given sub-region.

A secure digital management tool for managing real estate ownership and leases, consisting of: a tamper-proof enclosure containing a secure processing module; a biometric authentication module comprising a fingerprint sensor and an iris scanner, which are operationally coupled with a secure biometric engine to verify a user's physical identity; a document integrity module configured to generate a cryptographic hash of a digital document representing a certificate of ownership or a lease agreement; a smart contract engine configured to compile and execute the logic for legal transactions related to ownership in a Turing-complete virtual machine environment, the logic defining obligations, rights, payment schedules or renewal triggers encoded in the lease or ownership contract; a blockchain interface processing unit operationally connected to the document integrity module and the smart contract engine, wherein the blockchain interface processing unit is configured to anchor cryptographic hashes of the documents and the execution status of smart contracts in a decentralized ledger network; a secure element embedded in the secure processing module and configured to store cryptographic private keys and perform isolated signature and encryption operations without exposing confidential key material to general storage space; a communication subsystem that includes at least one of the following elements: a GSM / 4G module, a Wi-Fi 6 module and a GPS receiver, configured for secure network connectivity and geolocation binding of transactions; a touchscreen interface configured for displaying transaction forms, digital agreements, and interactive contract signing interfaces, the interface also including pen input for capturing digital handwritten signatures; and a secure audit trail subsystem configured to maintain a chronologically ordered, append-only log of all biometric access events, document hash generations, blockchain anchoring events, and smart contract lifecycle transitions, with the audit trail regularly hashed and anchored for forensic integrity in a decentralized blockchain network.

The invention provides a park greening polling maintenance method based on NFC and GIS park data association, and relates to the technical field of data processing, and the method comprises the steps: configuring a preset NFC label on a greening object, and binding the recognition information in the preset NFC label with the greening object position information registered in a GIS system; reading the NFC label through terminal equipment, obtaining identification information of the current greening object, and calling corresponding geographic positioning information and historical maintenance data in a GIS system according to the identification information; collecting field state information, including plant morphological parameters and surrounding environment condition information, of the current greening object, and comparing and analyzing the field state information with historical maintenance data; in combination with the maintenance standard of the corresponding greening object, generating specific maintenance task content, and uploading the specific maintenance task content to the management platform in real time through the terminal equipment to form a maintenance task record; the autonomy and accuracy of park greening inspection and maintenance are improved.

A system for multitask detection performs subject tracking by processing image frames from one or more video cameras deployed in a monitored environment. The system uses a neural network to detect human subjects in each frame and extracts feature sets for each subject. These features include a semantic center of the body and directional vectors extending to other body parts, such as the head or face, forming a subject-specific fingerprint. The system compares these fingerprints across frames to identify instances of the same subject over time. By correlating subject positions in image frames with the geolocation data of the capturing cameras, the system computes global coordinates for each subject. Using both the subject-specific fingerprints and spatial coordinates, the system determines trajectories of individuals, including transitions between camera views.