4809 results about "Location data" patented technology

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and / or location data. The system receives data from multiple vehicles in a fleet. The vehicle gateway device gathers vehicle metric data at a high frequency. Instead of transmitting a large amount of vehicle metric data at a fine level of granularity, the vehicle gateway device aggregates and buckets the vehicle metric data over a period of time (such as, every five minutes). The system uses the bucketed data for fleet management analysis.

Approaches presented herein provide for the use of language models to generate tokenized descriptions of physical environments. In at least one embodiment, sensor and / or observational data can be obtained for an environment and used to generate a set of perception data. The perception data can be analyzed, along with approximate positional data within the environment, to identify a set of aligned map data. The aligned map data and perception data can be provided as input to a trained language model, which can be trained to correlate and / or fuse the information to generate a single, consistent representation of the environment. The language model can output a tokenized description of the environment, which can be in a domain-specific language, that is a compact but robust textual description of the environment.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

A device capable of using data from multiple sensors to determine an estimated position / direction of a user with respect to the device. The device may use estimated position data, along with confidence data, that originated from a plurality of sensors to fuse the data to determine the user's estimated position and comprehensive confidence of the estimated position. The system may use the location information to perform beamforming / beam steering and / or other downstream operations using the comprehensive estimated position.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention relates to the technical field of medical intelligent scheduling, in particular to a patient flow data visualization adjusting system and method, and the system comprises a global dynamic sensing unit, a medical intelligent evolution center, a space-time mirror image twinning module, a multi-dimensional decision optimization matrix, and a risk closed-loop management and control unit. The global dynamic sensing unit gathers data to construct a diagnosis and treatment panorama; the medical intelligent evolution center is used for building a medical process evolution system to form a three-dimensional cognitive architecture; the space-time mirror image twinning module is used for constructing physical virtual space mapping, and forming a diagnosis and treatment resource space-time distribution path based on Internet of Things position data and a digital twinning technology; the multi-dimensional decision optimization matrix extracts patient flow data according to the space-time simulation path and a multi-target model, and generates a resource scheduling scheme; and the risk closed-loop management and control unit is used for evaluating the risk, marking congestion early warning and dynamically optimizing a regulation and control strategy and a threshold value. Therefore, the problems of data isolation, poor resource configuration efficiency, insufficient real-time performance and the like in the prior art are solved.

The invention relates to the technical field of cold-chain logistics information, and discloses a multi-mode AIGC cold-chain logistics information processing method and system, and the method comprises the steps: carrying out the feature extraction and feature enhancement of video data, temperature data and position data in a cold-chain logistics system based on a logistics information AIGC model, and obtaining an enhanced feature matrix; carrying out digital twinborn modeling to obtain a digital twinborn model of the logistics system, and constructing a state parameter space; performing deep reinforcement learning and information delay index calculation to generate a real-time data synchronization scheme; constructing a distribution network graph structure, and performing depth map network coding on the distribution network graph structure to obtain a distribution network representation vector; and performing multi-agent collaborative decision-making on the distribution network representation vector to obtain a cold chain distribution scheduling scheme, and updating the cold chain distribution scheduling scheme to the logistics system digital twinborn model. Therefore, intelligent scheduling and accurate control of the cold-chain logistics system in a complex scene are realized.

A method and apparatus for evaluating vulnerability of monopile foundations of offshore wind turbines are provided. The method includes collecting offshore wind farm location data and wind-wave characteristic data, and simulating a wind-wave time course according to the offshore wind farm location data and the wind-wave characteristic data; determining a wind-wave dynamic load based on the wind-wave course; obtaining lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters, and inputting the wind-wave dynamic load into a 3D finite element model; using the lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters as boundary conditions of the 3D finite element model; and giving a limit state of monopile foundations, and determining vulnerability of monopile foundations on basis of the dynamic response result of the monopile foundations and the limit state of monopile foundations.

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 invention discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

Methods and systems for managing operations of a data processing system are disclosed. To manage operations of the data processing system, a management controller of the data processing system may obtain validated location data for the data processing system via an out-of-band communication channel. The management controller may identify applicable policies based on the validated location data. If the management controller identifies a policy applicable to a geographical location of the data processing system, then the management controller may perform an action set to manage operation of the data processing system based on instructions associated with the applicable policy. The updated operation of the data processing system may increase the security of sensitive data managed by the data processing system.

The invention discloses an air pollutant concentration prediction method and related equipment thereof, and relates to the technical field of data processing. The method comprises the following steps: acquiring current pollutant concentration data, current meteorological data and target site geographic position data corresponding to a target environment monitoring site; taking the current pollutant concentration data, the current meteorological data and the geographic position data of the target station as target input data of the prediction model; wherein an input module of the prediction model is used for performing modal decomposition processing on input data to obtain multiple pieces of modal data; the time feature extraction module is used for extracting time feature data; the spatial feature extraction module is used for extracting spatial feature data; the space-time fusion convolutional network module is used for extracting space-time features; the output module is used for outputting a prediction result; determining a prediction result of the air pollutant concentration corresponding to the target environment monitoring station according to the output result; the method achieves the effects of improving the comprehensiveness and accuracy of a prediction result and expanding application scenes.

The invention belongs to the technical field of transportation, and particularly relates to a block chain-based transportation box full-process traceability management method and system, and the method comprises the steps of transportation box identifier generation, initial data chaining, transportation data collection, node verification recording, automatic abnormality response, and full-process traceability. According to the method, the unique block chain encryption address and node signature verification of the transport case are adopted, and the data are linked with a physical lock through Hash chain binding and encryption chips after being linked, so that the whole-process data cannot be tampered; an Internet of Things sensor is adopted to collect temperature and humidity / position data in real time, edge calculation is adopted to filter abnormal values, and intelligent contract threshold alarm and authority freezing are carried out, so that automatic collection and response of dynamic data are realized; a block chain timestamp and a chain structure are adopted to generate a space-time atlas, binding of an operation record and a responsible person signature and a fragmented block chain support cross-regional collaboration, and multi-agent collaboration tracing and accurate positioning are realized.

Methods and systems for managing a data processing system based on location data are disclosed. A management controller of the data processing system may provide the location data via an out-of-band communication channel to a geolocation management server tasked with mapping a location of the data processing system over time. The data processing system may identify an occurrence of an event based on the location data (e.g., a change in location of the data processing system), prompting the data processing system to perform an action set to update operation of the data processing system (e.g., to conform to location-based policies). Based on its updated operation, the data processing system may provide computer-implemented services that are more likely to be in compliance with local policies.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

A mobile system is provided for managing safety in a dynamic environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) process the data stream with aid of a machine learning algorithm trained model to generate a safety related result and feedback data, and (ii) deliver the feedback data to the object via the mobile tag device, and a mobile platform configured to move the mobile system in the environment.

This disclosure describes systems, methods, and devices related to performance measurements. A device may request a UE location from a location management function (LMF). The device may identify a UE location report from the LMF. The device may request performance measurements related to beam or timing advance from a first management service (MnS) producer. The device may identify a measurement report received from the first MnS producer. The device may request geographical data of a cell or base station from a second MnS producer. The device may identify the geographical data of the cell or the base station received from the second MnS producer.

The invention discloses a navigation method of a multi-modal mobile sorting robot and a sorting system, and relates to the technical field of robot navigation, when the light fluctuation in a sorting area exceeds the expectation, an image acquisition mechanism is triggered before a to-be-sorted article reaches a preset position; fusing the collected auditory data and visual data, extracting multi-modal features, and identifying the to-be-sorted articles according to the multi-modal features; after the to-be-sorted article is positioned through the visual data and the auditory data, if the deviation degree between the two pieces of position data exceeds the expectation, the position data of the to-be-sorted article is corrected; a sorting value is generated according to the to-be-sorted article information, and after the to-be-sorted article is marked according to the sorting value and the real-time position, a moving path for moving to the to-be-sorted article is planned for the sorting robot; and identifying and positioning the to-be-sorted articles by using the multi-modal data.

The invention discloses a gas leakage detection method, a program product and equipment, and the method comprises the steps: obtaining a collected continuous frame infrared image, and obtaining a multi-frame visible light image corresponding to the continuous frame infrared image; forming infrared input data of a pre-trained gas leakage detection model based on the continuous frames of infrared images, and forming visible light input data of the pre-trained gas leakage detection model based on multiple frames of visible light images; based on the infrared input data and the visible light input data, a gas fusion feature map is obtained through a gas feature extraction network in the gas leakage detection model; and based on the gas fusion feature map, identifying gas leakage position data through a gas position identification network in the gas leakage detection model.

The invention relates to an intelligent safety protection management method and system. The method comprises the following steps: acquiring behavior data and position data of personnel in an industrial site; analyzing the behavior data and the position data by using a pre-trained personnel behavior model to obtain a behavior recognition result; according to the behavior recognition result and the current operation state of the target equipment, judging a risk level corresponding to the personnel behavior; based on the risk level, a corresponding safety response strategy is matched in the edge computing node, a control instruction is generated according to the safety response strategy, and the control instruction is used for driving the target device to execute a corresponding response action; and the voice interaction module is used for acquiring voice input of an on-site operator, performing semantic recognition on the voice input to obtain a voice recognition result, and executing corresponding emergency control operation to cover a current control instruction when the voice recognition result meets a preset emergency instruction condition. The method has the effect of improving the accuracy of intelligent safety management in the industrial environment.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

Provided are a vehicle control method and apparatus, and a vehicle-mounted device, a vehicle and a storage medium. The vehicle control method comprises: on the basis of environmental perception data, position data of a vehicle, and map data, determining behavior trajectory information of a target object in an environment where the vehicle is located; sending the behavior trajectory information of the target object and ego-vehicle trajectory information of the vehicle to a server, so that the server processes the behavior trajectory information of the target object and the ego-vehicle trajectory information on the basis of a target model to generate planning and decision-making information, wherein the target model is a generative pretrained transformer and is obtained by means of performing training on the basis of a plurality of types of training sample data; receiving the planning and decision-making information fed back by the server, and on the basis of the behavior trajectory information of the target object and the planning and decision-making information, generating planning trajectory information and driving strategy information for the vehicle; and on the basis of the planning trajectory information and the driving strategy information, controlling the vehicle to travel. The safety of autonomous driving and the driving and riding experience of users can be improved.

The invention discloses an integrated circuit junction temperature and thermal resistance evaluation system and method based on a digital twinning technology, and the method comprises the steps: extracting a temperature cloud picture from a simulation calculation result, and extracting a thermal flow line diagram from the temperature cloud picture based on a heat dissipation path, carrying out discrete sampling on the heat flow line diagram by using a deep learning data analysis and coding technology, and then carrying out embedded coding on each piece of sampled heat flow point data (a flow point position and a heat flow point temperature); in this way, the rectangular frame position data of the hot spot region is automatically identified according to the graph walk significant aggregation representation between the data embedding coding features of each heat flow point. Through the mode, the influence of a fine structure (such as a connecting wire, a welding spot and the like) on a heat transfer path can be captured, the complex heat dissipation requirement can be better met, and it is ensured that the heat dissipation requirement is effectively met.