464 results about "Location map" patented technology

The invention provides an intelligent inspection method and system for fan blades based on an unmanned aerial vehicle. The method comprises the steps that a visible light camera, an infrared thermal imager and a laser radar are integrated on the unmanned aerial vehicle; generating a flight route covering the whole surface based on the fan blade three-dimensional model; when the unmanned aerial vehicle is controlled to fly along the flight route, visible light images, infrared temperature data and laser point clouds are synchronously collected; the visible light image, the infrared temperature data and the laser point cloud are input into a defect analysis model, the defect type and grade are identified through conjoint analysis, a temperature abnormal area is positioned, and geometric deformation is quantified; a health assessment report is generated, the health assessment report comprises a defect positioning map and a graded maintenance decision matched with defect grades, multi-source data are collected through integration of a visible light camera, an infrared thermal imager and a laser radar, and then fusion analysis and recognition are carried out, so that compared with a manual inspection mode, the intelligent level of fan blade inspection is improved, and the inspection efficiency is improved. And the inspection efficiency of the fan blade is also improved.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.

Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.

The invention provides an urban green land invasion and occupation degradation identification system and method based on computer vision identification, and the method comprises the steps: collecting multi-temporal images of an urban green land, sorting the images according to timestamps, and generating a difference atlas to reflect the dynamic change of the green land; carrying out vectorization processing on the dynamic information, establishing a digital model as a reference standard of a greenbelt boundary, and carrying out superposition comparison with the latest remote sensing image to obtain a corrected greenbelt boundary; using the target detection network to identify a non-vegetation coverage area in the multi-temporal image, generating a preliminary green land invasion location map, then performing shape regularization and size filtering, and optimizing the green land invasion location map; and based on the optimization graph, time and space distribution characteristics of each factor are analyzed, prediction is carried out in combination with the change trend of the non-vegetation coverage area, and a trend prediction result is generated. And finally, carrying out quantitative evaluation on a prediction result, and generating a comprehensive degradation severity report. According to the invention, the identification efficiency and identification precision of urban green land invasion and degradation are improved.

The invention discloses a handwritten mathematical formula recognition method based on attention coverage and position perception, which comprises the following steps: constructing a position perception module, and generating a character position map through weak supervised learning; a multi-scale attention coverage module is constructed, and attention calculation in a Transform decoding architecture is optimized; and fusing the feature map and the position sensing map, and inputting the fused feature map and the fused position sensing map into a Transform decoder to generate a LaTeX sequence. Character position information is explicitly extracted through the position sensing module, and the understanding of the model on a complex mathematical structure is enhanced; the attention drifting is reduced by a multi-scale attention coverage mechanism; the training cost is reduced and the model performance is improved by adopting a counting label weak supervised learning mode.

A computing system comprises a processor and memory storing instructions executable by the processor to obtain a sequence of image frames in a video. A target image frame of the sequence are input into a visual token embedding network of a trajectory-aware transformer to output a plurality of query tokens. A plurality of different image frames are input into a motion estimation network, the visual token embedding network, and a value embedding network to output a location map for each image frame, a plurality of key tokens, and a plurality of value embeddings, respectively. An image frame is selected the plurality of different image frames that has a closest similarity value from among the plurality of key tokens, and a super-resolution image frame is generated at a target time step as a function of an index query token, a value embedding of the selected frame, and the target image frame.

A computer-implemented method includes executing, via a flood simulation model, a computer simulation that simulates flooding within a target spatial area based on an input of a geospatial dataset and the rainfall intensity data of one or more flooding events associated with the target spatial area; determining normalized inundation scores for a set of geographical cells and one or more clusters of interconnected geographical cells based on executing the computer simulation; determining, via an optimization algorithm, a set of optimal sensor locations in the target spatial area based on the normalized inundation scores and the one or more clusters of interconnected geographical cells; and generating a sensor location map that includes the set of geographical cells and a set of geospatial markers identifying the set of sensor locations; and generating a visual output that displays the sensor location map.

The disclosure relates to determining a physical location of nodes in a wireless network comprising a plurality of nodes. Example embodiments include a method of determining a physical location of a plurality of nodes (102) in a wireless network (100), the method comprising: i) operating a first node (102m) of the plurality of nodes (102) in a scanning mode to detect a second node (102n) of the plurality of nodes (102) operating in an advertising mode within range of the first node (102m); ii) repeating step i) for other nodes of the plurality of nodes (102) to determine a list of node pairs that are within range of each other for each of the plurality of nodes (102) in the wireless network (100); iii) transmitting the list of node pairs from each of the plurality of nodes (102) in the wireless network (100) to a controlling node (101); iv) instructing by the controlling node (101) each of the node pairs to perform a channel sounding operation to determine a physical distance between the node pair; and v) constructing by the controlling node (101) a location map of the plurality of nodes (102) in the wireless network (100) based on the physical distance between each node pair.

The invention relates to the technical field of computer vision and satellite video processing, in particular to a satellite video dense vehicle tracking method and system. The method comprises the following steps: constructing a satellite video dense vehicle data set VDD-VEH containing a motion vector label, and providing supervision information for space-time modeling; designing a motion position map (MPG), mapping a target space position and a motion flow into a three-dimensional space-time diagram structure, fusing space-time consistency, appearance features and detection confidence by using a multi-feature edge weight (MFEW) strategy, and quantifying node association strength; global optimal trajectory association is realized by adopting integral linear programming (ILP), abnormal trajectories are eliminated by combining a trajectory optimization module (TRM), trajectory fractures are repaired, and long-time-sequence tracking stability is enhanced. The method is remarkably superior to the prior art in indexes such as MOTA and IDF1, the identity switching frequency is reduced by 55%, and the method is suitable for intelligent traffic monitoring and remote sensing video analysis and has high precision and cross-scene generalization ability.

The disclosure is directed to a system for identifying anomalies within a vehicle wash location such as within a car wash tunnel. In some embodiments, the system utilized artificial intelligent models such as computer vision to determine position, velocity, proximity, and / or dangerous conditions of vehicles and / or wash equipment at the vehicle wash location. In some embodiments, the system is configured to control a vehicle conveying path based on abnormal conditions. In some embodiments, the system is configured to identify if a vehicle has deviated from an expected orientation, where the system can control the wash equipment in order to avoid a collision. In some embodiments, the system is configured to create a model of the vehicle wash location from a vehicle wash diagram. In some embodiments, the vehicle wash diagram enables a user to enter wash equipment and can output a camera location map based on the diagram.

The invention relates to a cross-image associated information enhanced visual location identification method, a cross-image associated information enhanced visual location identification device and cross-image associated information enhanced visual location identification equipment. The method comprises the following steps: respectively extracting a database global descriptor of position image data in a map database and a query global descriptor of visual image data to be subjected to site identification, the multi-level feature mixing unit is used for carrying out multi-level feature fusion on the multi-level features in channel and space dimensions to obtain multi-level mixed features, and the multi-level region feature aggregation unit is used for carrying out multi-level region feature aggregation to obtain a region feature set; and carrying out self-enhancement on the regional feature set to obtain an image feature self-enhancement coding unit of a global descriptor, and identifying a site position in the visual image data according to the similarity between the query global descriptor and the database global descriptor so as to realize stable and high-accuracy positioning of a visual site identification task.

The invention relates to a complex terrain area typhoon wind field downscaling method and system based on deep learning. A data acquisition and standardization preprocessing module acquires wind field and terrain data and performs standardization preprocessing; a first typhoon wind field grid prediction module inputs the standardized wind field and topographic data into a multi-scale wind-topographic spatial correlation extraction network to generate a first typhoon wind field grid prediction value; a second typhoon wind field grid prediction module establishes a site space location map and a site feature correlation map according to meteorological observation site data, inputs standardized data into meteorological observation site space features to extract a meteorological observation site space linkage network, and generates a second typhoon wind field grid prediction value; a final typhoon wind field grid fusion prediction module performs weighted fusion on the generated first typhoon wind field grid prediction value and the second typhoon wind field grid prediction value to obtain a final typhoon wind field grid prediction value; according to the typhoon wind field downscaling method, the accuracy and efficiency of typhoon wind field downscaling in a complex terrain area are improved.

The invention discloses a dynamic Gaussian digital human image rendering method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: determining a target multi-view image frame from a multi-view RGB video image sequence according to a preset attitude, and constructing a deformable parameterized model according to the target multi-view image frame; constructing an attitude space driving attitude corresponding to the multi-view RGB video image sequence based on a three-dimensional attitude estimation technology, and generating a two-dimensional position map according to the attitude space driving attitude and the deformable parameterized model; performing three-dimensional Gaussian binding on the deformable parameterized model to obtain a local attribute of the three-dimensional Gaussian; training is carried out according to the two-dimensional position map, and a target StyleUNet neural network is obtained; and predicting the new attitude through the target StyleUNet neural network to obtain a dynamic Gaussian digital human image. In this way, the high-fidelity drivable high-frequency detail digital human image can be automatically rendered.

A marine navigation system for a marine vessel includes a positioning unit configured to detect a position of the marine vessel, a nautical chart database, a display being configured to present a view of a nautical chart information and a position of the marine vessel on the nautical chart, and a short range distance sensor module arranged on the marine vessel. The short range distance sensor module provides scanned data of a surrounding environment of the marine vessel. A control unit is operatively connected with the short range distance sensor module, the positioning unit, the nautical chart database and the display. The control unit processes the scanned data to provide a scanned view of the surrounding environment. The control unit is configured to present an augmented view of the nautical chart and scanned data on the display, and to indicate portions of the scanned data which does not match the nautical chart information in the augmented view.

Aspects of the subject disclosure may include, for example, accessing device usage data that can be generated when end user devices connect with the network; and analyzing the device usage data according to identification information of the deployable cell site, which enables determining or estimating a location of the deployable cell site. In one embodiment, this analysis can be based on device usage reports and network performance data. A performance report and / or location map can be generated for an area associated with the deployable cell site. Other embodiments are disclosed.

Provided herein is a method for verifying the identity and location of a putative user of a mobile computing device, including the steps of: photographing the user or a portion thereof with a first camera to generate a user image; photographing the environment with a second camera to generate an environmental image; transmitting the images to a remote receiver device associated with a remote processor; extracting a unit of image biometric information from the user image; comparing the unit of image biometric information to a stored unit of biometric information associated with the putative user's identity in a database of biometric information; and comparing the environmental image with a location image database, where the location image database includes images of known location.

The invention relates to the technical field of on-site safety management, and discloses a construction site on-site safety inspection system and method based on big data, and the system comprises a construction area construction information collection module, a construction area construction information evaluation module, and a construction area construction danger feedback module. Construction danger type intelligent analysis is carried out by storing different types of construction site danger image parameters based on big data in combination with an intelligent recognition algorithm and real-time inspection position image parameters of a current construction area, and accurate monitoring of the construction danger type of the on-site inspection position of the whole construction site is achieved. Construction site safety inspection area space position coordinate parameters are autonomously set, and coordinate data matching is carried out on the construction site safety inspection area space position coordinate parameters and current construction area real-time inspection position image parameters in combination with an intelligent search algorithm, so that a construction site safety inspection area where current construction dangers are located is accurately identified; partitioned, efficient and accurate positioning of the construction danger occurrence site of the construction site is achieved.

The invention discloses a pedestrian trajectory prediction method and device based on pedestrian scene interactive modeling, and the method comprises the steps: extracting the trajectory feature space of each pedestrian through employing a multi-feature map, obtaining the feature maps of the relative position, speed and acceleration, and carrying out the convolution of each feature map, superposing to obtain pedestrian interaction features; encoding the scene semantic segmentation map by using a SwinTransform, mapping the pedestrian coordinate sequence into a position map, and aligning and fusing the position map with the semantic features to obtain space-time scene representation; cross-modal fusion of pedestrian interaction features and scene features is completed through a bidirectional self-attention mechanism; predicting a bivariate Gaussian distribution parameter of a future trajectory by using a time extrapolation convolutional network, and performing end-to-end training by using negative log-likelihood loss; according to the method, pedestrian interaction behaviors and scene constraints are comprehensively considered, and the future trajectory of the pedestrian can be more accurately predicted in a crowded and complex scene.

The invention belongs to the technical field of small sample image segmentation, and particularly discloses a small sample image segmentation method and system based on low-rank reconstruction, and the method comprises the following steps: obtaining a support image and a query image, constructing a small sample image segmentation model, respectively extracting support features and query features, and constructing a small sample image segmentation model; calculating a feature correlation matrix between the support features and the query features, performing non-negative decomposition on the matrix to obtain a non-negative low-rank sub-matrix, reconstructing the feature correlation matrix, and mining a potential structure of the feature correlation matrix; and jointly interpreting and generating a position map of the target area by combining the spatial position information and the relation information of the feature correlation matrix, and inputting the generated position map, the support features and the query features into a decoder for decoding to obtain final segmentation prediction. By adopting the technical scheme, the potential structure of the feature correlation matrix is mined based on low-rank reconstruction, effective transmission of label information is promoted, and the segmentation performance of a small sample image segmentation task is improved.

The invention relates to the technical field of port monitoring, and discloses a monitoring and early warning platform for an unmanned operation area of a port, which comprises an image acquisition module, an environmental data acquisition module, a model construction module, a monitoring and early warning module and an early warning display module. BIM is combined with a design drawing and geographic information to build a three-dimensional digital model, space coordinate points contain attribute information, a coordinate system is unified through a coordinate conversion algorithm, and an abnormal position can be accurately positioned; according to image early warning, the abnormal state of an object is judged through a gray difference value, the number of pixel points and distribution density, environment early warning is analyzed from two aspects of whether an environment parameter exceeds a preset range and whether a change rate exceeds a threshold value, and potential risks can be found in time; an early warning signal is transmitted to a monitoring center, and key information is recorded in combination with a three-dimensional model, so that quick response is facilitated.

The invention discloses a stacker walking type RFID rapid checking method, device and equipment, and relates to the technical field of intelligent warehousing automatic checking, and the method comprises the following steps: obtaining a position sequence of a stacker on a walking path, and synchronously analyzing the recognition state of an RFID tag to obtain a tag reading position map; based on the tag reading position map, identifying a critical point of identification state change of the RFID tag in the position sequence; segmenting the walking path based on the critical point to obtain a tag identification section, and performing center offset evaluation on the tag identification section to obtain an optimal identification position point of the RFID tag; based on the optimal identification position point of the RFID tag, the polling trigger sequence is constructed, the RFID reader-writer is controlled to execute tag polling according to the polling trigger sequence, a final inventory list is obtained, and the problem that the tag frequently quits and joins identification polling due to dramatic fluctuation of tag reflection intensity in the walking process of the stacker is solved.

The present application provides for grouping user equipments (UEs) in a cluster and / or hierarchy. The packets may be based on a dynamically updated mean square error position / relative position map maintained at the transmission point. Furthermore, the grouping may be implemented in a hierarchical and cooperative manner such that some UEs in a higher level act as anchor UEs for other UEs in a lower level. In addition, the UE may be understood to be self-organized into a cluster of UEs. A cluster of UEs may be represented by a single representative UE to reduce signaling overhead between UEs in a given cluster of UEs and various anchor UEs and / or transmission points. Further, collaboration between UEs in the same cluster may be done by sensing and sharing between UEs a relative position estimate and a position estimate (MSE) associated with the relative position estimate.

A method includes, detecting a pointing motion made by an occupant of a vehicle and causing a photograph to be taken by a camera associated with the vehicle. The method also includes processing one or more of the photograph, a time the photograph was taken, location information of the vehicle at the time the photograph was taken, a photographing direction extending from the camera at the time the photograph was taken, or map data to obtain a candidate object of interest in the photograph and description information associated with the candidate object of interest. The method further includes causing a graphical user interface to be output by a display. The graphical user interface includes a geographical map, a location icon indicating a location on the geographical map at which the photograph was taken, a graphical object representing the photographing direction, and description information associated with the candidate object of interest.

The invention discloses a risk assessment method and device, electronic equipment and a storage medium, and relates to the technical field of security operation and maintenance and risk assessment. The method comprises the following steps: respectively acquiring a plurality of position images, a sound array and a temperature array of target production equipment in a target plant area, and analyzing the plurality of position images, the sound array and the temperature array to obtain an equipment risk assessment result; obtaining video data in the target factory area, and determining operation behaviors and material storage information of workers in the target factory area; determining a worker risk assessment result based on the operation behavior of the worker, and determining a material placement risk assessment result based on the material storage information; and determining a risk assessment result of the target factory based on the equipment risk assessment result, the worker risk assessment result and the material placement risk assessment result. According to the scheme, risk assessment can be rapidly and accurately carried out on the chemical factory, potential risks are identified, and help is provided for safety management of the chemical factory.

One embodiment of the present invention sets forth a technique for generating a geometry for a shape. The technique includes inputting, into a machine learning model, (i) a noise sample and (ii) one or more conditioning inputs. The technique also includes generating, via execution of the machine learning model based on the noise sample and the one or more conditioning inputs, a two-dimensional (2D) position map associated with the shape. The technique further includes generating a three-dimensional (3D) geometry for the shape based on the 2D position map.

Methods, systems, and techniques for utilizing image processing systems to measure objects, such as vehicles and damage to vehicles, include determining locations of waypoints within a source image of the object, reshaping a minimum area polygon that is defined by the waypoint locations to cover at least a portion of the depicted object within the image so that the reshaped at least a portion has an essentially uniform depth of field, and determining a warping transformation based on the reshaping. The methods, systems, and techniques further include applying the warping transformation to at least a portion of the source image to generate a warped image, and utilizing the warped image to measure areas of damage on the depicted object and the respective locations of the damaged areas, e.g., in conjunction with heat map and / or segmentation map of the source image, which may have been warped using the transformation.

The invention discloses a lane line detection method based on SDAMamba and FFCDConv, and relates to the field of lane line detection. The method comprises the following steps: preprocessing a training set lane line image to obtain an updated training set; improving the DAMamba model to obtain an SDAMamba model for extracting multi-scale features of the updated training set image; extracting depth multi-scale features by using a feature pyramid neck network; mapping the depth multi-scale feature to a Hough parameter space by using DHT and selecting a Hough feature corresponding to a lane; jointly inputting the Huffcharacteristics and the depth multi-scale characteristics into an FFCDConv module, predicting the characteristics of different lane instances, and obtaining a predicted lane position map through a lane decoder; and inputting the predicted lane position map into a linear layer to determine a final lane position, and obtaining a lane line detection image. According to the method, the problem of breakage easily occurring in a lane line detection result can be repaired in a targeted manner, and the confusion rate of adjacent lanes is remarkably reduced.

The invention belongs to the technical field of intelligent control, and particularly relates to a water supply network leakage monitoring system based on a big data model. The system comprises a data acquisition module, a water supply network leakage judgment module, a water supply network leakage positioning module and a water supply network leakage verification module. The data acquisition module collects pressure, flow, water quality and soil humidity data around the pipeline. And the leakage judgment module analyzes the leakage probability by using a big data model, and improves the judgment accuracy in combination with theoretical model correction. The positioning module determines the leakage position through topological database construction, pressure wave preliminary positioning, flow balance fine positioning and multi-data verification. The leakage verification module collects a suspected leakage position image, and judges whether leakage occurs or not through image feature enhancement, leakage feature recognition and time-space correlation verification. Compared with the prior art, the system can comprehensively collect data, accurately judge the leakage probability, accurately position leakage and reliably verify the leakage, effectively reduce water resource waste and guarantee water supply safety.

The invention discloses a heat distribution pipeline rendering deviation correction method and device based on an unmanned aerial vehicle picture and a medium, belongs to the technical field of heat distribution pipeline inspection, and aims to solve the problems that picture information and unmanned aerial vehicle and mounting state information are not synchronized easily when an existing unmanned aerial vehicle inspects a heat distribution pipeline, and the heat distribution pipeline is not corrected. And the position of the underground heat distribution pipeline in a picture is deviated, so that the inspection effect is influenced. The method comprises the following steps: carrying out key point identification and matching on a front frame image and a rear frame image shot by an unmanned aerial vehicle, and determining a perspective transformation matrix based on key point pairs; according to the perspective transformation matrix, performing mapping processing on pixel coordinates selected in the rear frame image on the front frame image to obtain front frame GIS coordinates of the front frame image; performing correction processing on the flight state of the unmanned aerial vehicle under all-directional distance and angle factors to obtain unmanned aerial vehicle parameters; and performing image drawing on the pipeline position to obtain a final pipeline position image.