714 results about "Location systems" 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.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

The invention relates to the technical field of fault detection, in particular to a heating and ventilation system fault positioning system and method based on big data, and the system comprises a multi-source sensing module, a disturbance feature module, a path modeling module, a frequency spectrum matching module and a fault positioning module. In the method, a real-time disturbance sequence is constructed through time window segmentation and parameter offset calculation, separation of an active response chain and an abnormal propagation path is realized through a directional joint state vector and a topological relation table, and time asynchronism of multi-device signal transmission is eliminated by adopting a dynamic time warping algorithm. In combination with a real-time parameter bidirectional verification mechanism of a frequency domain main frequency band energy mark, a valve opening degree and a pump rotating speed, the problem of path confusion in a multi-node parameter coupling scene of a traditional method is solved, the tracing efficiency of concurrent faults in a complex pipe network system is improved, the adaptability limitation of a single-dimensional threshold mechanism to equipment performance degradation is overcome, and the method is suitable for a complex pipe network system. And the error positioning probability caused by signal delay superposition is reduced.

The invention provides a power line fault distance measurement system and method, and belongs to the technical field of power line detection. The system comprises monitoring terminals deployed at two ends of a line and a background processing system, and each monitoring terminal comprises a sensor module used for detecting a traveling wave current signal related to a power line fault in the line; the data acquisition module is used for extracting the arrival time of a traveling wave head according to the detected traveling wave current signal; the GPS time synchronization module is used for adjusting the local clock of the data acquisition module, reducing the time synchronization error of the two monitoring terminals and correcting the arrival time of the traveling wave head; and the background processing system is used for calculating a traveling wave time difference and a junction wave velocity normalization processing result according to the corrected arrival time at the two ends of the line, and determining a fault position coordinate and a fault type. According to the invention, collaborative optimization of fault positioning precision and type identification in a hybrid power line environment is realized.

The invention relates to the technical field of neural networks, and further relates to a power grid topology fault positioning system based on a graph neural network. The system comprises a power grid topology feature extraction unit used for regarding a power grid as a graph network and carrying out real-time data acquisition on each node of the power grid, and each element in a feature matrix represents the electrical characteristics of a certain node of the power grid at a certain time; the multi-layer graph neural network model unit is used for taking the feature matrix as the input of a first layer as a feature extraction result; the fault identification and positioning unit is used for calculating fault probability distribution based on the feature extraction result; and calculating the credibility of the obtained fault position. According to the method, the modeling capability of the system on the complex power grid topology is improved, the real-time performance and robustness of fault detection are enhanced, and accurate positioning of the fault area and position is realized.

The invention belongs to the technical field of power grid power transmission, and discloses a power grid intelligent power transmission line fault positioning system, which comprises the steps of collecting power operation data, spatial data and environmental data; correcting the power operation data based on the constructed power correction model to obtain corrected power data; performing topology establishment on the corrected power data and the spatial data by adopting a neural network algorithm to obtain a power grid topological graph, and performing graph decomposition on the power grid topological graph by adopting an ant colony algorithm to obtain B topological sub-graphs; constructing a fault prediction model for predicting a fault type; performing fault prediction on the B topology sub-graphs through a fault prediction model, marking the topology sub-graphs with predicted fault types as fault sub-graphs, positioning fault points in the fault sub-graphs through a particle swarm algorithm, and determining final fault point positions; and the positioning speed of the fault point location is greatly improved.

The invention discloses a server link fault positioning system and method, electronic equipment and a storage medium, and relates to the technical field of server link tests.Physical layer data and protocol layer data of a data collection point are collected through a collection assembly, a test assembly is controlled to conduct server link testing, the test assembly comprises a control module and an analysis module, and the control module is connected with the analysis module. The test instruction is issued to the server link through the control module, when the server link fault is monitored, the analysis module acquires the physical layer data and the protocol layer data of each data acquisition point from the acquisition assembly, and determines the fault position and / or the fault type of the server link according to the acquired data, so that the automatic link quality evaluation is realized, and the efficiency is improved. And the fault position and the fault type can be determined, so that the problems of difficulty in accurately positioning the fault position and distinguishing the hardware link fault and the software error through a single pressure test and low troubleshooting efficiency caused by manual fault positioning in the prior art are solved, and the technical effect of improving the troubleshooting efficiency is achieved.

Systems, methods, and non-transitory computer readable media including instructions for managing privacy in an extended reality environment are disclosed. The instructions include receiving image data of a physical environment from an image sensor associated with a wearable extended reality appliance; accessing data characterizing virtual objects in the physical environment, the data representing a first and a second virtual object; accessing privacy settings classifying a first physical location of the first virtual object as private, classifying a first appliance as approved for presenting private information, and classifying a second appliance as non-approved for presenting the private information; and based on the privacy settings, simultaneously enabling a presentation of an augmented viewing of the physical environment, such that the first appliance presents the first and the second virtual objects in the physical environment, and the second appliance presents the only second virtual object, in compliance with the privacy settings.

Apparatus, systems, and methods for mapping utility lines in utility potholing operations are disclosed. Pothole mapping apparatuses, systems, and methods may be employed in utility locator devices and utility locating systems to map potholed utility lines. Data generated via pothole mapping may be used to calibrate utility depth measurements in utility locator devices and systems. Further, information revealed about utility lines during potholing procedures may be used to train neural networks and utilize artificial intelligence in generating predictions regarding the positions and characteristics of utility lines.

The invention relates to the technical field of power grid fault positioning, in particular to a rapid power grid fault positioning system based on digital twinning and AI, which obtains multi-dimensional operation data of each node of a power distribution network, constructs a structured data set, reflects the state of the system, and performs joint modeling on node features and a topological structure through a graph neural network. And reasoning to generate candidate fault nodes. And further constructing a digital twinborn model containing load flow calculation and disturbance injection, performing analogue simulation on candidate nodes, extracting an electrical quantity change rate, performing fuzzy similarity matching with operation data in a fault window, and accurately positioning a fault node. Through cooperation of three stages of prediction, simulation and matching, the problems of weak complex topology perception, poor perturbation identification, positioning offset and the like of the existing method are effectively solved, and the fault positioning precision and robustness are improved.

Accurately identify a location based on images, even indoors SOLUTION: In this system, a control unit 22 of a server 20 that acquires an image captured by a terminal device 10 identifies the position of the terminal device. The server includes a database 23 that stores a large number of 3D maps showing each area and feature data related to the 3D maps. The feature data includes vectorized images of the 3D maps and related text information, context information of the 3D maps, etc. A map narrowing-down unit 222 of the control unit vectorizes the acquired images, etc., searches the vectors against feature data stored in the database, and also provides the acquired images and context information of the 3D maps to the LLM to narrow down the 3D map candidates (eliminates 3D maps with similar shapes). A map matching unit 221 matches the shapes of the narrowed-down 3D map candidates to accurately identify the position and orientation.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention discloses a power grid fault positioning system and method based on traveling wave distance measurement, relates to the technical field of fault analysis, and solves the technical problems that traveling wave feature analysis, temperature compensation and high-precision synchronization technologies are not effectively integrated, and rapid and accurate fault positioning of a smart power grid is difficult to meet. Through the technical combination of dynamic temperature compensation, high-precision time synchronization and intelligent fault classification, the bottlenecks of traditional traveling wave distance measurement in the aspects of precision, reliability and intelligence are systematically solved, temperature sensors are deployed at key nodes of a cable, the temperature is monitored in real time, the wave speed is dynamically corrected through a formula, the overall error is reduced, and the fault location accuracy is improved. Meanwhile, an optical fiber two-way time transmission method is combined, transmission delay errors are reduced, synchronization reliability is improved, finally, a mapping library is constructed through laboratory simulation and field data, a Euclidean distance matching algorithm is combined, fault reasons are automatically recognized, and rapid positioning of the faults and accurate recognition of the fault reasons are achieved.

A method includes sending, by a master beacon device and one or more beacon repeater devices, ultra-wideband (UWB) beacon frames. The UWB beacon frames are transmitted as interleaved pairs of UWB beacon frames. Each interleaved pair includes a first UWB beacon frame and a second UWB beacon frame. For each interleaved pair, the first UWB beacon frame and the second UWB beacon frame are transmitted with a master time delay. The method further includes receiving, by one or more tag devices, at least one of the interleaved pairs of UWB beacon frames.

The invention discloses an intelligent fault positioning system and method based on a knowledge graph. The system comprises an incidence relation management module, a knowledge graph generation module, an intelligent fault positioning module and a fault report generation module. Wherein the association relationship management module is used for storing and managing imported resource nodes and association relationships among the resource nodes, and uploading the stored resource nodes and the association relationships to the knowledge graph generation module. And the knowledge graph generation module is used for generating a knowledge graph with a blood relationship according to the association relationship among the resource nodes in the association relationship management module. And the intelligent fault positioning module is used for calling the knowledge graph generated by the knowledge graph generation module to position and mark abnormal resource nodes with faults. And the fault report generation module is used for automatically generating a corresponding fault report according to the abnormal resource node with the fault and sending the fault report to a user.

The invention relates to a distributed fault positioning system and method for a power transmission and distribution line, and the system comprises the power transmission and distribution line, an acquisition unit group, a collection unit, a communication base station, a cloud server, and a data terminal. The acquisition unit group is used for monitoring current, self state parameters and the like of the power transmission and distribution line, the collection unit is used for receiving and sending communication data with the acquisition unit group, the communication base station is used for receiving and sending communication data with the collection unit, and the cloud server is used for communicating with the communication base station and the data terminal. And the data terminal is equipment with a communication function at the user side. According to the scheme, the application range of a line fault positioning system is expanded, including mountainous areas without mobile communication network coverage, a mode of collecting and installing equipment distributed calculation is adopted, the accuracy and efficiency of fault positioning are improved, the line patrol time is saved, the time of line power restoration is shortened, and the reliability of a power supply system is effectively ensured.

The invention provides a full-autonomous cell operation system based on body intelligence, and relates to the field of cell operation, the system comprises a system hardware architecture and a system software architecture; the system hardware architecture comprises a micromanipulation execution system, a precision positioning system, a multi-mode sensing system and an intelligent calculation and control center. The micromanipulation execution system is used for cell environment interaction, the precision positioning system is used for sample bearing and environment control, the multi-modal sensing system is used for providing key information, and the intelligent calculation and control center is used for operation decision making; the system software architecture comprises a sensing layer, a planning layer, an execution layer and an interaction layer. According to the application, cell recognition, tool selection, path planning, operation execution and result feedback can be automatically completed without manual intervention; through tool autonomous switching and strategy dynamic adjustment, various operations such as cell clamping, injection, controllable deformation and micro-assembly can be completed, and the problems that traditional cell operation depends on manual work, efficiency is low, and consistency is poor are solved.

The invention discloses a power distribution network line fault positioning system based on multi-source information fusion, which relates to the technical field of power system operation and maintenance and comprises a data acquisition module, a data preprocessing module, an electric network topology management module, a fault prediction module, a fault judgment module and a man-machine interaction and alarm module. The method comprises the following steps: acquiring running state information from a power distribution network in real time, generating a structured multi-dimensional information database according to the running state information acquired by a data acquisition module, and performing time alignment, format standardization and feature vector construction on the acquired running state information to generate the structured multi-dimensional information database; according to the method, a fault section is preliminarily judged according to fault features, a fault state prediction model is constructed, a multi-dimensional information database is used for estimating state variables of the model, and the method aims at overcoming the defects that in the prior art, fault positioning fuzziness and reliability are insufficient due to the fact that a single information source is relied on.

A cargo controller is disclosed herein. The cargo controller includes a wireless emergency module (412) including an ultra-wide band radio, the wireless emergency module (412) configured to communicate with a plurality of cargo emergency stations of a cargo handling system using the ultra-wide band radio, a wireless control module (418) configured to communicate with a cargo control station of the cargo handling system, a processor, a memory operatively coupled to the processor. The memory includes instructions stored thereon that, when executed by the processor, cause the processor to receive ultra-wide band radio signals from the plurality of cargo emergency stations, determine a location within the cargo handling system based on the received ultra-wide band radio signals, and transmit the location to the cargo handling system.

A real-time location system comprises sensors and an upstream device communicating with the sensors. Sensor pairs of the sensors include a transmitting sensor and a receiving sensor. Each sensor is assigned as the transmitting sensor in turn, while all other sensors are designated as the receiving sensors and collect signal strength data. The upstream device determines a labelled dataset based on the signal strength data for the sensor pairs and zone labels associated with the signal strength data. The RTLS ML model is trained based on the labelled dataset.

A geolocationing system and method for providing awareness in a multi-space environment, such as a hospitality environment or educational environment, are presented. In one embodiment of the geolocationing system, a vertical and horizontal array of gateway devices is provided. Each gateway device includes a gateway device identification providing an accurately-known fixed location within the multi-space environment. Each gateway device includes a wireless transceiver that receives a beacon signal from a proximate wireless-enabled personal locator device. The gateway devices, in turn, send gateway signals to a server, which determines estimated location of the wireless-enabled personal locator device.

The invention discloses a power distribution network fault positioning method and positioning system, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: collecting multiple groups of historical data of different faults of a power distribution network, obtaining the missing intervals of voltage and current sequences in each group of historical data, and positioning the missing intervals to the front, middle and back of the whole sequence; different interpolation methods of different missing intervals are obtained by combining the length of the missing interval, the distance from the starting point position of the missing interval to the previous nearest missing interval and the distance from the ending point position of the missing interval to the next nearest missing interval, an abnormal value is marked for a sequence after interpolation is completed, and the sequence is equally divided into S < front > and S < back >; the abnormal values are repaired by utilizing ELM, a standard data set belonging to the fault is obtained, clustering processing is carried out on data in the standard data set, irrelevant clusters are removed, and a specific position is positioned according to the similarity between the fault and each group of faults in the target cluster. The power distribution network fault positioning method effectively improves the power distribution network fault positioning precision.

Systems and methods include a medical device that scans for wireless broadcasts based upon received input or satisfying a scanning criterion. The medical device, based upon the scan, identifies data wirelessly broadcast from a control device. The medical device outputs information to the interface identifying the control device. The medical device receives input from the interface selecting the control device. The medical device receives a command from the control device. The medical device physically interacts with a user based upon the command received from the control device. The medical device sends wireless confirmation of pairing to the control device. The medical device subsequently sends wireless confirmation of unpairing to the control device. The medical device performs a scan for wireless broadcasts in response to the unpairing, based upon received input or satisfying the scanning criterion.

The invention provides a novel wireless blind detection method, device and equipment, a medium and a computer program product, and the method comprises the steps: converting a received air interface analog signal into time domain stream data, and sequentially carrying out the synchronous processing and fast Fourier transform of the time domain stream data, and obtaining frequency domain stream data; determining a candidate set space to be subjected to blind detection based on the frequency domain stream data; performing network identifier detection on the candidate set space to be subjected to blind detection to obtain a balanced data block corresponding to a target identifier; and determining a blind detection result of the air interface analog signal based on the balanced data block. According to the method, data stream processing and data block balancing are sequentially carried out on the air interface analog signal, and under cooperation of the data stream processing and the data block balancing, load balancing of blind detection of the new wireless passive positioning system is achieved.

A cargo controller is disclosed herein. The cargo controller includes a wireless emergency module including an ultra-wide band radio, the wireless emergency module configured to communicate with a plurality of cargo emergency stations of a cargo handling system using the ultra-wide band radio, a wireless control module configured to communicate with a cargo control station of the cargo handling system, a processor, a memory operatively coupled to the processor. The memory includes instructions stored thereon that, when executed by the processor, cause the processor to receive ultra-wide band radio signals from the plurality of cargo emergency stations, determine a location within the cargo handling system based on the received ultra-wide band radio signals, and transmit the location to the cargo handling system.

The invention belongs to the technical field of packaging jet printing, and discloses an external packaging carton two-dimensional code jet printing quality online monitoring and error correction process, which comprises the following steps: building a vision, laser, spectrum and pressure four-source sensing positioning system in a preprocessing stage, comprehensively collecting carton material and form related data, and forming a pre-calibration closed loop in cooperation with microsample jet printing verification; in the jet printing process, a high-speed imaging system of self-adaptive light supplement works synchronously, after an image is preprocessed through a combinatorial algorithm, various defects such as blooming and scratches are accurately recognized through a ResNet34-Transform-GAN fusion model, and then an LSTM neural network is used for predicting the development trend of the defects and conducting early warning; a three-level parameter configuration system of cloud decision, edge execution and twin simulation is utilized, an edge calculation unit uploads data such as materials, sizes and flatness collected in real time to the cloud, historical optimal parameters of the corresponding materials are called, and then dynamic adjustment is conducted in combination with the conveying speed.

The invention, which relates to the technical field of the wireless communication network, discloses an AI-based fault route rapid positioning system comprising: a whole network flow topology data synchronous acquisition module used for obtaining synchronized original data from network elements in the wireless communication network; the disturbance characteristic time sequence extraction module is used for generating a characteristic time sequence based on the synchronized original data; the flow topology coupling effect quantification module is used for combining the characteristic time sequence with the network topology index data to generate a real-time fault fingerprint sequence; and the fault source reverse reasoning module is used for acquiring a preset fault fingerprint database, calculating the fingerprint difference degree and judging the prototype fingerprint with the minimum difference degree so as to generate a fault positioning report. According to the method, the fault source can be globally and accurately positioned, the soft fault can be sensitively detected, the positioning precision and robustness are improved by fusing multi-dimensional information, and adaptive parameter optimization and learning expansion can be realized.

The invention discloses a low-cost transportable system for UUV path planning and control experiments. The low-cost transportable system comprises a shore-based unit, a buoy unit, an underwater vehicle unit, a zero-buoyancy cable and a software stack, the UWB tag, the router, the GNSS and the rocker receiver are integrated in the buoy, a sealing waterproof design is adopted, and the buoy can stably float on the water surface and is in wired connection with the underwater vehicle through the zero-buoyancy cable; the three-dimensional position of the underwater vehicle is obtained, the upper computer and the UWB base station are deployed on a shore base, the upper computer interacts the calculated position with the router in the buoy through wireless communication, and a stable and efficient communication link is constructed. The system can be quickly arranged in indoor and outdoor environments, supports closed-loop path planning and control experiments, can effectively replace expensive USBL or indoor dynamic capturing and positioning systems, remarkably reduces experiment cost, and improves experiment flexibility and portability.