2677 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.

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.

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.

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 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 leasing equipment full life cycle monitoring method and system based on the Internet of Things, and belongs to the technical field of equipment operation and maintenance monitoring, and the method comprises the steps: obtaining real-time state data of leasing equipment in an operation process through a multi-source sensor; performing feature extraction on the real-time state data to obtain various feature vectors, and constructing a multi-dimensional feature vector set; inputting the multi-dimensional feature vector set into a random forest model, and judging whether the leasing equipment is in an abnormal state or not in combination with a space-time clustering algorithm; if yes, fault prediction is carried out on the leasing equipment in combination with an abnormal detection result, historical operation data and a baseline model, and a fault probability value of the leasing equipment is obtained; the future position of the leasing equipment is predicted based on the position data, an electronic fence boundary is set for the leasing equipment, if the future position exceeds the electronic fence boundary, an early warning notification of trajectory deviation is generated, and the system finally realizes trajectory and fault visual presentation in a thermodynamic diagram mode, so that the equipment operation and maintenance intelligence and management precision are improved.

The invention relates to the technical field of transformers, in particular to a transformer component fault diagnosis method based on machine learning, which comprises the following steps: analyzing medium data acquired by a transformer component, comparing a dielectric loss factor sequence with offset characteristics, training and generating an offset time sequence characteristic set, screening abnormal time periods and aggregating key discharge characteristics, and performing fault diagnosis. And obtaining an abnormal clustering signal group, concluding a trend evolution sequence, performing layered optimization on a path structure, and outputting fault link positioning data. According to the method, various parameters such as dielectric loss, discharge, temperature rise, gas and vibration are integrated, abnormal distribution is actively screened, key node trend change is captured in real time, historical and real-time characteristics in different operation scenes are fused, and efficient analysis of complex link evolution and implicit anomalies is realized. And the fault evolution relationship among the nodes is output in a structured manner, direct application of a diagnosis result in full-link traceability and multi-node trend research and judgment is supported, and the fault diagnosis pertinence and the hidden danger recognition capability are effectively improved.

Methods, apparatus, systems, and articles of manufacture are disclosed for device authentication in a dedicated private network. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to utilize the machine readable instructions to generate first credentials associated with a first network based on second credentials associated with a second network, the first credentials including first location data corresponding to a dedicated private network (DPN), cause a mobile device to program a programmable subscriber identity module (SIM) of the mobile device based on the first credentials, and permit the mobile device to access the DPN based on a determination that second location data corresponding to the mobile device and included with the programmable SIM corresponds to the first location data.

Examples provide improved methods for managing wireless network connectivity for a mobile device. Examples include receiving a network map that defines geographic availability of a first wireless network; determining that the first wireless network is accessible within a travel segment of a travel plan based on comparing geographic location data associated with the travel segment to the geographic availability of the first wireless network from the network map; creating a network plan that includes the travel segment and an associated connectivity waypoint, the connectivity waypoint defining when to alter connectivity status with the first wireless network; and causing the mobile device to alter connectivity status with the first wireless network based on proximity to the connectivity waypoint.

The invention provides a charging pile cluster power coordination method, device and equipment and a storage medium, relates to the technical field of charging pile optimization, and is applied to a charging pile power distribution system which comprises a plurality of charging piles. The method comprises the following steps: acquiring current state data, historical use data and position data of each charging pile in different time periods, and determining corresponding priority weight data according to the position data and the historical use data; based on a preset prediction model, obtaining use probability data of the corresponding charging pile according to the historical use data and the corresponding current state data; obtaining corresponding power reference data according to the power grid dispatching plan data, the current state data and the use probability data; and obtaining a power adjustment strategy of each charging pile according to the current state data, the priority weight data and the power reference data based on a deep reinforcement learning model. According to the method, the prediction model and the deep reinforcement learning technology are combined, and intelligent and efficient power coordination is realized.

The invention discloses a root cause analysis method based on an IT operation and maintenance system, and the method comprises the steps: obtaining the physical position data and real-time environment data of IT equipment, carrying out the matrix construction through a dual topological relation based on the physical position data and the real-time environment data, and obtaining an IT equipment spatial topological matrix containing environmental impact factors. And obtaining operation state data of the IT equipment, performing spatio-temporal conjoint analysis and dimension reduction based on the operation state data and the IT equipment spatial topology matrix to obtain IT equipment dynamic prediction data, and obtaining an IT equipment state dynamic prediction curve according to the IT equipment dynamic prediction data. If the IT equipment state dynamic prediction curve monitors abnormity, a fault propagation path is generated through a reinforcement learning algorithm, and IT equipment fault root causes are determined in combination with a multivariate fault knowledge graph and a causal graph model. According to the method, the problem of early warning lag of the IT operation and maintenance system can be solved, and the problem that root cause positioning has high dependence on artificial experience is solved.

The invention discloses an agricultural scene three-dimensional reconstruction method based on multi-modal sensor fusion, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring multi-modal data of a target scene based on a tracked robot, wherein the multi-modal data comprises image data acquired by a binocular camera, point cloud data acquired by a laser radar, time sequence inertial data acquired by an inertial measurement unit and position data acquired by a Beidou positioning system; extracting different modal data features in the multi-modal data, and carrying out heterogeneous fusion on the different modal data features to obtain multi-modal data fusion features; and agricultural scene three-dimensional reconstruction is carried out by using a three-dimensional dynamic construction method based on space-time-semantic joint embedding. According to the invention, the environment scene modeling precision of the agricultural robot under the interference of muddy, raised dust and vibration can be improved.

The invention provides a multi-source information fusion unmanned aerial vehicle marine target tracking method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining image data, and extracting the features of a target region; unmanned aerial vehicle attitude data, radar position detection data and AIS identification data are obtained, attitude disturbance correction is executed, and time axis alignment is performed on the data; calculating the visual feature matching offset of the adjacent frames, and comparing the visual feature matching offset with the radar azimuth change trend; constructing a candidate target spatial position set, and executing distance convergence screening and spatial overlapping constraint processing of a geometric projection range on the candidate target spatial position set; motion direction continuity comparison is executed, and target position data is generated; generating a track control instruction for controlling the flight direction and speed of the unmanned aerial vehicle according to the target position data, and adjusting the subsequent flight path of the unmanned aerial vehicle according to the track control instruction; according to the invention, autonomy and accuracy of unmanned aerial vehicle marine target tracking are improved.

The invention discloses an electric power cross-operation dynamic risk prediction and early warning method, system, equipment and medium based on multi-modal data fusion, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting multi-source data of a cross-operation site in real time, including personnel spatial position data, equipment state data, environmental parameters and operation flow information; performing multi-modal fusion processing on the multi-source data, and constructing space-time risk association features; constructing a dynamic risk factor matrix, performing fusion calculation on the static risk reference value and the dynamic correction value, and dynamically updating the weight of each factor along with time; predicting a risk change trend based on the combination of a time sequence prediction model and a graph structure modeling method; and generating a three-dimensional visual risk thermodynamic diagram based on a prediction result, and triggering a multi-level early warning mechanism when a risk value exceeds a threshold value. The image is reconstructed through multi-modal fusion processing, and key edge features can be recovered in complex environments such as low illumination, high dynamic range and fast motion.

A system includes a network node device operable to dynamically connect to different networks and different types of networks based on factors including latency and noise levels in each network. The system allows for low power transmission of data packets as well as obfuscation of transferred data packets by splitting packets for a single file across multiple nodes in a mesh network, where the mesh network then recombines the signals to externally convey the data. The system leverages the use of LoRa communication across one or more networks to communicate critical data, such as location data for particular devices, at long ranges with minimized risk of detection.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments for determining, in a remote deposit system, whether a deposit attempt is illegitimate (e.g. fraudulent). Whether the deposit attempt is illegitimate may be assessed based on one or more of the following processes: comparing location data to a location parameter determined from past deposits, comparing an image capture location with a deposit location, and analyzing image-of-image characteristics obtained through image processing to identify whether an image associated with the deposit attempt is an image of an image. In some embodiments, a remote deposit status related to acceptance of the deposit attempt may be provided in real-time

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance decision-making method and system for a complex environment, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance. The method comprises the steps of S1, environment perception and data acquisition, S2, multi-source data hierarchical fusion processing, S3, complex environment obstacle recognition and risk assessment, S4, dynamic obstacle avoidance path decision and optimization, and S5, obstacle avoidance execution and real-time adjustment. Omnibearing acquisition of obstacle distance, environment images, unmanned aerial vehicle attitude and position data is realized; the method combines an improved YOLOv8 model (adding a CBAM attention mechanism), strengthens the obstacle feature extraction capability under complex weathers such as rain and fog, strong light and dust, can accurately distinguish static obstacles such as buildings and trees from dynamic obstacles such as pedestrians, vehicles and other aircrafts, and solves the problems that a single sensor perceives blind areas and is insufficient in precision in a complex environment.

The invention relates to the technical field of multi-sensor fusion positioning, and discloses an outdoor robot multi-sensor fusion method. Comprising the following steps that robot operation data are obtained through multiple sensors in the running process of a robot; taking the preprocessed operation data as measurement input of an unscented Kalman filter state estimation process, and obtaining first position data after fusion processing; learning the noise characteristics of each sensor by using a long-short-term memory network of a sliding window, and compensating the position deviation of the robot to obtain second position data; performing time synchronization processing on the first position data and the second position data, and inputting the first position data and the second position data into an improved double-layer multi-model adaptive estimation framework; the improved double-layer multi-model adaptive estimation framework adopts a double-layer structure; and obtaining final real-time position data. The method can inhibit noise and drift of a single sensor, reduces error accumulation caused by factors such as environment shielding, improves positioning precision, robustness and continuity, and is suitable for scenes such as outdoor driving robots.

The present invention discloses a system and method for processing trade finance documents and performing automated compliance screening. The system comprises a computing device, and a database for storing trade finance documents. The system processes documents using OCR to extract text and positional data, generating structured document representations via a layout-aware AI model. An AI classifier module categorizes documents based on content, layout, and domain-specific roles, while a semantic verification module aligns document data with master Letter of Credit templates. A rule management module validates compliance against international trade standards, and a financial crime risk control module performs real-time checks against external sanctions, vessel, and dual-use goods databases. The system further determines and reports discrepancies, anomalies, and compliance issues. The system supports heterogeneous layouts, multi-language documents, and integration with banking APIs.

Described is a system for generation location visualization on a map interface by identifying a current location of a user that is initiating an interaction function of an interaction client; identifying a map corresponding to the current location of the user; identifying one or more map tiles associated with the map; receiving historical location data of the user that is associated with the current location of the user; converting the historical location data into an overall polygon that is comprised of a plurality of polygons based on the identified one or more map tiles; and displaying the map with the plurality of polygons on a user interface.

The invention relates to the technical field of crane control, and discloses a long-distance collaborative operation method for multiple gantry cranes, which comprises the following steps: constructing a global coordinate system; acquiring position data of dynamic equipment of each crane in real time and mapping the position data to a global coordinate system to form an equipment coordinate set; synchronously processing all equipment coordinates through a central processing unit, and generating a position sharing data set; meanwhile, environment obstacle data are collected and mapped to a global coordinate system, and spatial constraint conditions are established; generating a first path scheme by adopting a collaborative planning algorithm based on the position sharing data and the spatial constraint condition; monitoring the operation deviation in real time in the execution process, and obtaining an optimized second path scheme through deviation correction; and finally, dynamically monitoring environment change, triggering obstacle avoidance path updating when the environment change exceeds a preset threshold value, and outputting a collaborative operation scheme. According to the method provided by the invention, multi-machine cooperative path planning and dynamic obstacle avoidance under a cross-spatial scale are realized, the conflict risk is reduced, and the operation continuity and safety are improved.

The invention provides an intelligent tactile stick interaction method and system based on multi-modal perception and edge calculation, and relates to the technical field of intelligent auxiliary equipment. The method comprises the following steps: acquiring environment and position data through a camera, a GPS and an inertial measurement unit; performing multi-modal data fusion and deep learning identification by using an edge calculation module to generate a dynamic environment map; performing real-time path planning and obstacle avoidance based on a map and a destination; analyzing a user instruction through voice recognition and interactively adjusting a path; haptic and auditory bimodal feedback is provided through a vibration module and a voice synthesis module according to a navigation result; and when an emergency obstacle is detected or a user actively alarms, a local and remote alarm mechanism is triggered. The system correspondingly comprises an intelligent tactile stick terminal and a cloud server. According to the invention, accurate and real-time perception and intelligent navigation of the environment are realized, visual and natural interaction experience is provided, an effective emergency help channel is established, and the safety, independence and convenience of travel of visually impaired people are significantly improved.

The present disclosure provides an in-vehicle monitoring and tracking system for rental vehicles that detects smoking events within a vehicle cabin and logs these events with associated location data. The system employs specialized detection algorithms to accurately identify various types of smoke while minimizing false positives. Additional functionality includes accident detection through rapid deceleration monitoring, and animal presence detection through acoustic sensing. When events are detected, the system can either store the information for later retrieval or transmit real-time notifications with precise location data to rental company servers, enabling prompt response to policy violations and facilitating vehicle recovery after accidents.

The invention discloses a marine algal bloom disaster early warning system based on big data, particularly relates to the technical field of marine algal bloom disaster early warning, and is used for solving the problems that an existing algal bloom disaster monitoring means is lagged, early warning response is not timely and multi-source environmental data is difficult to fuse. The method comprises the steps of obtaining multi-source marine environment data including algae bloom position data and ocean current motion data, performing time-space synchronous calibration and standardization processing on the data, establishing a risk early warning area identification model, and calculating a dynamic correction coefficient in combination with an environment modulation factor. Key early warning features such as propagation trend intensity, influence range and duration are extracted to construct a multi-dimensional early warning signal vector, the multi-dimensional early warning signal vector is input into a pre-trained early warning level prediction model to generate a prediction early warning level, and a dynamic early warning trigger condition is set based on economic value and ecological sensitivity; and finally generating a grading early warning signal and pushing the grading early warning signal to a disaster early warning terminal in real time, thereby realizing high-precision, grading and dynamic early warning of the algae bloom disaster.

In some implementations, the techniques described herein relate to a method including: receiving a data log entry associated with a driver that includes a service provider location and a timestamp; identifying a vehicle associated with the driver based on the data log entry by identifying the vehicle includes applying a machine learning model to the data log entry and a vehicle database; loading a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry includes applying a rule-based optimization algorithm to a historical service provider database; and transmitting a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

This disclosure provides systems, methods, and devices for machine learning techniques for improved training, such as for vehicle surroundings analysis. In one aspect, a method is provided that includes receiving image data and position data from the area surrounding a vehicle, determining initial feature data based on the received data, and determining updated feature data for training a first model. The updated feature data may be determined based on uncertainty measures for portions of the initial feature data, occluded regions within the initial feature data, or combinations thereof. In certain aspects, the first model may be trained using knowledge distillation techniques. Other aspects and features are also claimed and described.