250 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.

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 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 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 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.

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.

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 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.

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.

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.

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 provides a visual modeling method for a ship pipe network program based on a YOLOv8 target detection technology, and the method comprises the following steps: S1, constructing a human-computer interaction visual interface of a ship pipe network system in the form of an SVG base map and a control position map through employing a WPF interface generation technology; s2, identifying the pipe network elements in the human-computer interaction visual interface by using a YOLOv8 target detection technology, and obtaining an identification result; s3, using a Python optimization algorithm to carry out classification arrangement and model recombination on identification results, generating an architecture model containing pipe network elements and control spatial positions and attribute parameters by a system, and storing the architecture model in a standard CSV file form; and S4, converting the architecture model stored in the CSV file into a mathematical model by using a C # programming language, performing real-time calculation, and outputting a calculation result to a human-computer interaction visual interface. According to the invention, the integrated operation of the ship pipe network system from design to simulation is realized, the design efficiency is obviously improved, and the operation is more convenient.

The invention provides a four-way shuttle vehicle scheduling method and system which are suitable for a storage space with multiple layers, the scheduling system can pre-configure a goods location map in a mapping relation with an actual storage space, real-time information including position information and state information of a four-way shuttle vehicle is obtained in real time, and after a new task needing to be executed is generated, the task needs to be executed. The four-way shuttle vehicles on the same layer or different layers are called to execute the tasks according to the time required for completing the queue tasks on each layer, so that the circulation efficiency of goods in the storage space can be ensured, unnecessary layer changing actions of the four-way shuttle vehicles can be reduced as much as possible, congestion of the elevator is avoided, and the service life of the elevator is prolonged. And a foundation is laid for smoothness and efficiency of a four-way shuttle vehicle dispatching system and a warehousing system.

The invention belongs to the technical field of petroleum geological exploration, and relates to a logging lithology intelligent identification method and system based on well network position map structure learning and feature fusion, and the method comprises the steps: obtaining the original data of logging in a target work area, and carrying out the standardization preprocessing, the original data comprises logging data, well position coordinate data and lithology label data; according to the original data after standardization preprocessing, a well network position map structure is established; performing feature extraction on the well pattern position map structure by using a map neural network to obtain high-dimensional features; fusing the high-dimensional features with original logging features, and obtaining an enhanced feature set to train a lithology classification model; and utilizing the trained lithology classification model to predict the lithology of a to-be-identified well section, and outputting a lithology identification result of the to-be-identified well section. The lithology identification accuracy and the geological rationality can be obviously improved.

The invention discloses a spatial domain identification method and system based on spatial multi-omics data. The method comprises the following steps: obtaining an expression profile and a spatial position coordinate through spatial multi-omics sequencing, and preprocessing the expression profile and the spatial position coordinate; decomposing and extracting a shared low-dimensional incidence matrix of the multi-omics data by using an anchor concept; constructing an intercellular similarity matrix based on the spatial coordinates, and generating a spatial location map by combining the k-nearest neighbor map and the high-order adjacency matrix; integrating multi-base clustering results by adopting an element-by-element weighting strategy to form an integrated graph; solving an optimal low-dimensional feature through a double-graph regularization objective function (fusing Laplacian matrix constraints of a spatial position graph and an integrated graph); and finally realizing high-precision spatial domain clustering. The method breaks through the limitation that a traditional method ignores spatial dependency and integrated interference, effectively fuses the high-order relation and spatial information of multi-omics data through anchor concept decomposition and a double-graph regularization framework, and remarkably improves cross-scene robustness; the method is suitable for biomedical scenes such as tumor microenvironment analysis and cell heterogeneity analysis.

The invention provides an online comprehensive detection, analysis and evaluation method and system for a power equipment bearing, and the method comprises the steps: obtaining four-dimensional operation parameters of the bearing in real time, including a temperature and vibration value, an audio frequency spectrum feature, a dynamic maintenance period parameter and a bearing load rate parameter; establishing a correlation model of specific frequency band energy change and vibration value fluctuation in the audio frequency spectrum characteristics through space-time correlation analysis, injecting a dynamic maintenance period parameter as a time domain attenuation factor into the correlation model, and dynamically weighting based on a bearing load rate parameter to generate a health degradation index; when the health degradation index exceeds a dynamic threshold value, generating a pre-maintenance instruction, and outputting a maintenance strategy containing a fault positioning map; a pre-maintenance instruction and a maintenance strategy are packaged according to an industrial communication protocol, and a correlation analysis view and an early warning window are dynamically embedded in a distributed control system interface, so that the accuracy of bearing online monitoring is effectively improved.

The invention relates to the technical field of agricultural automation, and discloses a seed leakage real-time detection method and system for an automatic sugarcane planter, and the method comprises the steps: collecting the distance measurement data and position signals of each seed metering channel in real time through a multi-channel sensor, and obtaining an initial distance measurement data set; calculating the relative distance difference between the channels based on the initial ranging data set, and determining the zero drift distance; when the zero drift distance exceeds a preset threshold value, datum point correction is carried out on the target calibration channel, and calibrated ranging data are obtained by fusing adjacent channel data; performing cross validation on the calibrated data, and judging an accumulative error level; channel cooperation parameters are adjusted according to the accumulative error level, distance measurement values passing verification are integrated through a data fusion method, and an optimized relative distance matrix is obtained; and mapping seed leakage point coordinates through the matrix, generating a position map, extracting reseeding target points, and determining a final seed leakage positioning result. The accuracy of seed leakage detection is improved.

The invention provides a wafer testing method, system and device and a medium, and the method comprises the steps: carrying out the appearance scanning of the back surface of a to-be-tested wafer based on a plurality of preset positioning identifiers on the to-be-tested wafer, so as to generate a position map for representing the position information of each crystal grain on the to-be-tested wafer; storing the position map and the wafer number corresponding to the position map to a cloud server; loading a position map stored in the cloud server into a test machine according to the wafer number; and putting the to-be-tested wafer into the test machine table to execute a test based on the loaded position map. According to the invention, the wafer testing efficiency can be effectively improved, and the productivity can be improved.

The invention relates to the technical field of environment monitoring, and discloses an environment-friendly environment monitoring system, which collects static and dynamic data through a fixed monitoring station and a mobile device, realizes frequency adaptive calibration by adopting a linear interpolation or spline interpolation algorithm, and constructs a fusion data matrix of time-space alignment; matching an optimal environment evaluation model based on an optimization algorithm (such as an eagle algorithm), and dynamically selecting a prediction algorithm in combination with pollution characteristics; a differential response strategy is triggered through a multi-stage early warning mechanism (yellow / orange / red), a pollution source positioning map and an emergency scheme are synchronously generated, and'monitoring-analysis-disposal 'closed-loop management is realized. The method can be applied to the scenes of urban atmosphere, river water quality, industrial area multi-parameter pollution and the like, the monitoring precision (the omission ratio is reduced by more than or equal to 25%), the response speed (the early warning push is less than or equal to 8 seconds) and the resource utilization efficiency (the energy consumption is reduced by 20-35%) are effectively improved, and a high-reliability solution is provided for intelligent environmental protection.

The invention provides a multi-physics field monitoring CT metal artifact suppression method based on artificial intelligence, which comprises the following steps: carrying out an indoor rock physics hydraulic fracturing experiment, and carrying out multi-physics field monitoring; constructing a training data set, wherein the data set comprises CT data not containing the metal artifacts of the piezoelectric ceramic sensor, CT data containing the metal artifacts of the piezoelectric ceramic sensor, and a position map of the piezoelectric ceramic sensor; constructing an artifact suppression module based on two domains and three channels, wherein the artifact suppression module uses two types of domain information of an image domain and a chordal graph domain; training a detection network by using the training data set; inputting the preprocessed CT data containing the metal artifacts of the piezoelectric ceramic sensor into an artifact suppression module to obtain corresponding CT data without the metal artifacts of the piezoelectric ceramic sensor and a position map of the piezoelectric ceramic sensor; and updating the data set and the artifact suppression module. According to the multi-physics field monitoring CT metal artifact suppression method based on artificial intelligence, CT metal artifacts can be suppressed, and the CT image quality is improved.

The application relates to an interpretable pedestrian prediction method introducing expert experience in a complex interactive environment, comprising the following steps: acquiring pedestrian information in an environment, wherein the pedestrian information comprises relative position, speed and acceleration information; determining expert experience coefficients of relative position relationships, relative speed relationships and relative acceleration relationships based on the pedestrian information; constructing interpretable position graphs, speed graphs and acceleration graphs based on the expert experience coefficients and the pedestrian information; performing topological structure coding on the position graphs, the speed graphs and the acceleration graphs based on a multilayer graph convolutional neural network to obtain hidden states containing spatial topological information; and performing space-time coding on the hidden states containing the spatial topological information, decoding by using a decoder to obtain predicted pedestrian trajectories. Compared with the prior art, the application has the advantages of fine consideration of interactions between pedestrians, accurate pedestrian trajectory prediction in a complex interactive environment and strong interpretability.

A self-organizing mesh network system is disclosed, comprising: at least one master node generating a timing reference signal; a plurality of regular nodes deriving time synchronization from the timing reference signal; and a wireless medium for communicating location and timestamp information among the plurality of regular nodes, The plurality of regular nodes may be configured to each use communicated location and timestamp information of nearby nodes to independently generate a location map of the nearby nodes. The plurality of regular nodes may be configured to accept an additional regular node. The plurality of regular nodes may be configured to allow a node of the plurality of regular nodes to exit the plurality of regular nodes. The plurality of regular nodes may each use communicated location and timestamp information of the plurality of regular nodes to independently generate a location map of each of the plurality of regular nodes.

Methods and systems for determining a signal-to-noise metric for locations of interest on a specimen are provided. One or more statistics of non-defect signals from background patch images in a test image that are similar to a patch image of a location of interest in the test image are determined. The background patch images are found by searching a reference image for patch images that are similar to the location of interest patch image and finding the corresponding patch images in the test image. The signal of the location of interest in the test image and the one or more statistics are used to determine a signal-to-noise metric for the location of interest. The signal-to-noise metric can be used in applications such as defect annotation, deep learning (DL) model tunability, DL model repeatability, and novel defect detection.

The invention discloses a packaged PCB layout reference mark positioning method based on deep learning. The method comprises the following steps: acquiring component data in a PCB layout, and marking the component data; establishing a topological graph structure, a vertex position graph structure and an angle relation graph structure according to the component data; building a multi-stream graph neural network, and performing training, testing and verification by using the data set; the multi-stream graph neural network comprises three parallel sub-networks, a feature fusion module and a classifier; and utilizing the trained, tested and verified multi-stream graph neural network to realize the reference identification positioning of the packaged PCB layout. According to the method, graph neural network input representation is adopted, the problem of information loss existing when the layout is converted into a pixel picture for recognition is avoided, higher precision and efficiency are achieved, and a brand new solution is provided for PCB layout reference positioning.

The present application relates to inland waterway inspection method, system and storage medium, including navigation mark detection and water level detection, navigation mark detection includes: step 11. Data acquisition;Step 12. Navigation mark identification;Step 13. Navigation mark positioning;Step 14. The real coordinates of navigation mark are inquired in the preset navigation mark data table, if the real coordinates of navigation mark have corresponding navigation mark in the preset navigation mark data table, then the current position information of the navigation mark is recorded;Step 15. Corresponding detection report is generated;Water level detection includes: step 21. Image acquisition;Step 22. Obtain the water gauge position image;Step 23. The water gauge position image is filtered, smoothed and denoised;Step 24. The water gauge scale and the image information with similar RGB values are extracted by setting threshold, and the scale is extracted by character extraction method, and the final water level information is obtained.The present application can automatically judge whether the navigation mark is damaged, the position is deviated, whether there is missing condition and water level.