3778 results about "Location data" patented technology

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.

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.

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.

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 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 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.

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.

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 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.

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.

Methods and systems for managing a data processing system are disclosed. To manage the data processing system, a management controller of the data processing system may obtain location data for the data processing system via an out-of-band communication channel. The location data for the data processing system may include different types of location data obtained via different location generation sources. The management controller may identify whether the different types of location data are consistently reporting location status of the data processing system. If the location data is determined to be consistent, then the management controller may provide a location report based on the location data using the out-of-band communication channel to a server to update a location status of the data processing system.

The invention discloses a photovoltaic module cleanliness evaluation method and system based on multi-dimensional data fusion. The method comprises the following steps: acquiring multi-dimensional data of a photovoltaic power station, including generating capacity data, power station state data, irradiation data, meteorological data, geographic position data, cleaning record data, weather forecast data and economic index data; preprocessing the data; constructing a multi-dimensional feature; a pre-trained Transform architecture time sequence model is adopted to carry out training; evaluating the cleanliness state of the photovoltaic module in real time and predicting the change trend; and determining the optimal cleaning time and generating decision suggestions based on the cleanliness evaluation result and economic analysis. Through multi-dimensional data fusion and a large model algorithm, precise evaluation and scientific decision making of the cleanliness of the photovoltaic module are realized, the evaluation accuracy is improved by 15-20%, and the method is suitable for photovoltaic power stations at different geographic positions and under different climate conditions.

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

The invention discloses a digital identity verification method and device based on a zero-trust architecture, terminal equipment and a storage medium, and belongs to the field of network security, and the method comprises the steps: obtaining an identity verification result according to user information and multi-factor identity verification information; according to the identity verification result, generating a session token and creating a session; collecting behavior data, equipment fingerprint data and geographic position data of the user during the session in real time, and dynamically adjusting the access authority stored by the session token according to the behavior data, the equipment fingerprint data and the geographic position data; each time a request sent by a client is received, the zero-trust architecture gateway determines the current session state of the server by verifying the current session token. Therefore, through the implementation of the invention, various identity authentication technologies can be organically integrated, continuous identity authentication under a zero-trust architecture is realized, and the security of digital identity authentication is improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous decision, provides an unmanned aerial vehicle autonomous decision reinforcement learning method, system and device based on multi-objective optimization, and a storage medium, and solves the problems of limited dynamic task distribution capability and poor decision capability of an unmanned aerial vehicle. The method comprises the steps that based on infrared data and radar data, a moving track prediction sequence of a suspicious target in a target area is generated, a moving probability thermodynamic diagram is constructed, and the target area comprises a patrolled area; performing Doppler frequency shift analysis on the radar data to generate a target velocity vector field; and in combination with the position data of the unmanned aerial vehicle, outputting an unmanned aerial vehicle autonomous decision reinforcement learning result of a dynamic segmentation scheme containing an unpatrolled region through a region re-division reinforcement learning model based on a reward function for multi-objective optimization. The reward function is used for reflecting area coverage rate maximization, target tracking success probability maximization and path energy consumption minimization. According to the invention, autonomous intelligent decision-making of the unmanned aerial vehicle on the unpatrolled area is realized.

The invention provides an intelligent meteorological service method and system triggered by meteorological risk pre-assessment, and relates to the technical field of meteorological disaster early warning, the method constructs a description vector library of various historical meteorological disasters, provides rich and high-quality data support for the training of a general large model, and improves the early warning efficiency of the meteorological disasters. A disaster risk pre-assessment large model is obtained through fine tuning and retraining of a general large model, so that various meteorological disaster risks can be identified and assessed more accurately. After a user inputs the multi-modal prompt data, the multi-modal prompt data comprises at least one of the following data: position data, scene image data and scene video data, and the model can quickly obtain a meteorological disaster risk assessment result, so that the method gets rid of the technical problem that a meteorological risk estimation area is limited. In addition, under the condition that the assessment result is determined to have the meteorological disaster risk, the method can also trigger the intelligent meteorological service based on the assessment result, and the intelligent meteorological service can effectively improve the pertinence of meteorological disaster early warning.

The invention relates to the technical field of satellite orbit prediction, and discloses a satellite orbit prediction method based on a physical information neural network, a terminal and a medium. The method comprises the following steps: constructing a sample set based on satellite position data, sun position data and moon position data in the same time period; constructing a physical information neural network; the network takes satellite historical position data as input to predict a satellite orbit position at a future moment; training a physical information neural network by using the sample set, and executing a satellite orbit prediction task by using the trained physical information neural network; the trained loss function comprises data loss, physical loss and smoothness loss; the data loss is used for measuring the difference between network prediction output and actual observation data; the physical loss is jointly calculated by the earth aspheric perturbation, the three-body gravitational perturbation and the solar light pressure physical perturbation borne by the satellite; the smoothness loss is used for constraining the smoothness of the predicted satellite orbit. According to the invention, the prediction precision and robustness of the satellite orbit are improved.

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 provides a vehicle identity anonymous authentication method in a space-air-ground fusion vehicle-mounted network. The method comprises the following steps: carrying out multi-source data acquisition; generating a dynamic area identifier based on the vehicle real-time position data, and performing dynamic updating according to a vehicle moving threshold value; vehicle equipment identification information is acquired, a salted hash value is generated in combination with the dynamic region identifier, and an anonymous certificate containing dynamic effective duration is generated; extracting spatio-temporal data fluctuation parameters according to the environmental perception data, and extracting delay trend parameters and channel stability parameters according to the regional network state data; the weight is dynamically adjusted according to the link type and the time period type, and the dynamic trust quantized value of the vehicle is calculated through weighted summation; differential authentication is executed based on the dynamic trust quantized value of the vehicle and the anonymous voucher, and the differential authentication comprises the steps that lightweight verification is adopted when the vehicle is highly trusted, two-factor authentication is started when the vehicle is low trusted, and link timeout redundancy authentication is combined. According to the method, the trust evaluation accuracy can be improved, and the dynamism of a privacy protection mechanism is enhanced.

The invention provides a highway engineering quality real-time monitoring method and system based on digital twinning, and the method comprises the steps: collecting construction data, and carrying out the preprocessing of the construction data, and obtaining a behavior time sequence and a coverage rate index sequence; constructing a behavior graph, performing quality prediction on each behavior segment node through a graph neural network to obtain a predicted quality score, and performing coverage rate index sequence correction to obtain an actual quality score; mapping the actual quality score to a spatial position to obtain a spatial quality tensor, wherein the spatial position is a three-dimensional position obtained by combining equipment position data and a compaction horizon number; performing structural consistency analysis on the spatial quality tensor, identifying and marking a topology abnormal region, and outputting a structural consistency scoring tensor; and generating a visual twin layer based on the spatial quality tensor and the structural consistency scoring tensor, and performing graph level labeling and section mapping on the abnormal region.

The invention provides a lightning proximity prediction method based on a space-time separation convolutional neural network. The method comprises the following steps: S1, acquiring satellite infrared brightness temperature observation data and lightning positioning data; s2, performing spatial resolution adjustment and standardization processing on the satellite infrared brightness temperature observation data; gridding and de-noising processing is carried out on the lightning positioning data; s3, defining a prediction task; s4, establishing a space-time sequence prediction model; based on the processed data, storing the processed data in a numpy file format according to a time point sequence, dividing the processed data into a training set, a verification set and a test set, and loading the training set, the verification set and the test set to a space-time sequence prediction model for model training to obtain an optimization model; and based on the prediction task, using the optimization model to carry out lightning approaching prediction to obtain lightning prediction data. According to the method, the development of cumulus cloud is captured by using satellite infrared brightness temperature observation data and brightness temperature difference data, so that the problem that thunder and lightning inception cannot be effectively predicted is solved.

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

The invention discloses a method, a system and a device for predicting hoisting of a balance beam of a crane. The method comprises the following steps: acquiring operation data of the balance beam in an operation space; establishing a measurement range model according to the position data; calculating an adjustment value of each lifting hook according to the inclination angle data and the spatial position data; according to the data of the horizontal distance between the balance beam and the surrounding space object, judging and determining a safety area of the operation of the balance beam; calculating the stress and strain of the balance beam by using the structural parameters of the balance beam; calculating an attitude angle in the lifting operation process by using acceleration data of the lifting operation of the balance beam; and analyzing the calculated data by using a fusion algorithm to obtain a balance beam hoisting operation model, a monitoring index and a prediction result. The system comprises modules corresponding to the method, the device comprises an ultrasonic distance measuring sensor, an inclinometer, an acceleration sensor, a processing module and a display device, the operation efficiency can be guaranteed, and the operation safety can be improved.