273 results about "Spatial relationship" patented technology

The application discloses a sea area change monitoring method, device and equipment and a storage medium, and relates to the technical field of sea area monitoring. The method comprises the following steps: performing super-resolution reconstruction based on target sea area boundary data at a set monitoring frequency to obtain remote sensing images of the target sea area; extracting scattered planar vector patches of culture facilities from the remote sensing images; determining culture range patches of the culture facilities according to the outer convex surface boundary geometry of the midpoint element patch in the scattered planar vector patch; analyzing NetCDF sea current multidimensional data, generating format grid sets including different direction flow rates and wind speed value vector fields according to the step length of the set monitoring frequency; determining a regression coefficient map according to the spatial relationship between the different direction flow rates and the wind speed, and monitoring the culture facilities by using the regression coefficient map. The application takes into account the influence of flow rates, flow directions and wind speeds on the position distribution of the culture facilities, and can efficiently and accurately monitor the temporal and spatial changes of the culture sea area by combining the analysis of the NetCDF data.

This application discloses a method and system for identifying internal defects in a reactor pressure vessel, a computer device, and a computer-readable storage medium. The method includes the steps of: collecting image data of the pressure vessel surface; constructing a light irradiation angle attention mechanism to create a feature map using the images in the dataset and generating a spatial attention map based on the spatial relationships of the feature maps; and improving a YOLOv5 model using the light irradiation angle attention mechanism and identifying surface defects in the pressure vessel using the improved YOLOv5. It features high defect identification efficiency and a low detection miss rate. It effectively addresses common problems when performing defect detection under various light irradiation conditions. This ability to accurately identify and distinguish defects is crucial for ensuring the safe operation of the pressure vessel, especially during routine maintenance and inspections.

Systems and methods are provided for processing X-ray images, wherein the methods are implemented as a software program product executable on a processing unit of the systems. Generally, an X-ray image is received by the system, the X-ray image being a projection image of a first object and a second object. The first and second objects are classified, and a respective 3D model of the objects is received. At the first object, a geometrical aspect like an axis or a line is determined, and at the second object, another geometrical aspect like a point is determined. Finally, a spatial relation between the first object and the second object is determined based on a 3D model of the first object, a 3D model of the second object, and the information that the point of the second object is located on the geometrical aspect of the first object.

This invention discloses a 3D scene segmentation and interaction method based on 3D Gaussian, comprising: Step 1, instance discovery: inputting 3D Gaussian scene data, dividing the 3D Gaussian points into structurally coherent instance-level Gaussian groups to obtain refined instances; Step 2, instance and scene semantic assignment: selecting representative viewpoint images of refined instances and inputting them into a visual-language model to generate semantic description labels for the instances; filtering instance pairs in the scene through spatial geometric relationships, and clarifying the spatial relationship description of instance pairs through a large model, constructing a static scene graph that integrates geometric proximity relationships and instance semantic relationships, forming a structured description of all instances; Step 3, natural language-driven instance localization and interaction: receiving user input commands, combining the instance semantic description, the static scene graph, and the real-time viewpoint direction relationship during the query to perform multi-dimensional matching, determining the target instance ID, and executing interactive operations.

This invention discloses an artificial intelligence-based method for monitoring soil erosion risk. To address the problems of untimely identification of soil erosion risk, difficulty in quantifying disturbance information, and insufficient early warning accuracy within the responsibility area of ​​production and construction projects, this invention acquires high-resolution remote sensing images and divides the responsibility areas into prevention and control units. It utilizes a disturbance identification model to identify pixel-level engineering disturbances and extract disturbance features such as disturbance area ratio, disturbance morphology, and spatial relationship between disturbances and drainage channels. Combining rainfall, topography, soil, vegetation, and soil and water conservation measures data, it calculates key factors such as rainfall erosivity, vegetation cover management, slope length and gradient, soil erodibility, and measure factors. These factors are then substituted into a modified general soil loss equation to obtain baseline soil erosion. The baseline soil erosion, disturbance features, key factors, and rainfall forecast are input into a deep residual network model to obtain target soil erosion risk indicators. Finally, based on risk classification thresholds, the risk level of each prevention and control responsibility unit is output, achieving the technical effect of unit-scale soil erosion risk monitoring and early warning.

The application provides a millimeter wave radar and video fusion tower clearance real-time monitoring method, which comprises a video monitoring camera system and a millimeter wave radar system; first, the two systems are installed at the bottom of the cabin and rotated to point to the blade clearance area, realizing image acquisition and radar reflection point data acquisition in the blade movement cycle; then the controller system calculates the blade tip clearance value according to the collected image data and radar reflection points, qualitatively evaluates the deviation of the two according to the spatial relationship between the blade tip trajectory line and the blade tip reflection point, and ensures the high-precision clearance monitoring of the blade tip and the tower. The clearance monitoring method of the fusion of the two sensors can realize the functions of mutual checking of the accuracy between the systems in normal weather, selection of the clearance for unit control in severe weather, stronger environmental adaptability and working condition adaptability, and avoids the problems of failure of a single sensor due to environmental influence or low clearance accuracy.

This invention relates to the interdisciplinary field of medical image analysis and artificial intelligence, and particularly to a method, device, and medium for detecting non-occlusal abnormalities based on oral images. The method first acquires oral images and determines their abnormality category: a first abnormality (dependent on the characteristics of a single tooth) or a second abnormality (dependent on the spatial relationships between multiple teeth). Based on the category determination, an adaptive differential detection process is executed: if it is a first abnormality, a two-stage process of tooth position detection and single-tooth recognition is used to output a detection result associated with a precise FDI number; if it is a second abnormality, an end-to-end target detection model directly outputs the abnormality type and location region; if it is a mixed category, the two processes are executed in parallel and the results are intelligently integrated. This invention achieves automated, precise, and structured detection of non-occlusal diseases.

This invention relates to the field of geographic information service security control technology, and discloses a method, system, device, and medium for hierarchical management and control of WMS maps. The method generates a cacheKey corresponding to the user's identity and caches the permission context when processing GetCapabilities requests, and performs layer filtering and access path rewriting on service capability documents. In subsequent GetMap requests, the permission context is restored based on the cacheKey, and transparent return, pass-through, or clipping is performed according to spatial relationships. In GetFeatureInfo requests, pixel coordinates are mapped to geographic rectangles, spatial verification is performed, and attribute results are filtered according to a field whitelist. This solution achieves collaborative management and control of layers, spaces, and attributes without modifying the backend WMS service.

The application discloses a kind of environment simulation method and system based on binocular stereo vision measurement, it is related to scene simulation technical field, and its technical solution points are: the method includes by constructing the physical imaging model in left and right optical elements in binocular stereo vision camera, and left and right view is obtained based on the physical imaging model;Determine the disparity map between the left and right view, determine scene depth image based on the disparity map;Three and more scene depth images generated by three and more different binocular stereo vision cameras corresponding to the scene are acquired, and the three and more scene depth images are fused, to construct the three-dimensional simulation model corresponding to the scene based on the depth image after fusion.The application can accurately reflect the depth level and spatial relationship of scene, and the three-dimensional simulation model finally constructed is more realistic, and can strongly support virtual reality, three-dimensional measurement and other applications.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform or cancel an uplink transmission in a subband full duplex (SBFD) resource based on whether the indicated uplink beam is enabled or restricted for SBFD. A network entity may monitor for or refrain from monitoring for an uplink transmission in an SBFD resource based on whether the indicated uplink beam is enabled or restricted for SBFD. A UE may be configured with a first uplink beam for half-duplex uplink resources and a second uplink beam for SBFD resources. The UE may perform uplink transmissions scheduled in SBFD resources using the first uplink beam. A UE may use a single uplink beam configured for both half-duplex uplink resources and SBFD resources, or a single uplink beam may be configured and restricted from SBFD resources, and the UE may cancel a scheduled transmission in an SBFD resource.

This invention relates to the field of bridge construction technology, and in particular to a digital installation method and system for steel anchor beams. The method includes: deploying positioning targets on the steel bracket; installing several binocular camera modules on the anchor beam; constructing a three-dimensional model of the steel bracket and anchor beam, clarifying the fixed spatial relationship between the positioning targets, binocular camera modules, and anchor beam, and calibrating and associating the three-dimensional model with the on-site construction coordinate system; deploying several BeiDou measurement reference stations to provide a basis for real-time position adjustment of the steel bracket during installation by measuring the coordinates of the positioning targets in real time; and activating each binocular camera module, which acquires images of the positioning targets in real time to provide a basis for real-time position adjustment of the anchor beam during installation. This application can effectively improve installation accuracy, overcome measurement fluctuations caused by environmental interference, meet the high-precision requirements of modern engineering, reduce the probability of safety accidents, solve the problem of work stoppages due to environmental constraints in traditional methods, and ensure the controllability of the construction period.

The application provides a spatial perception enhancement and pathological image classification method for processing class imbalance, aiming to enhance the model's perception of spatial information and process class imbalance. The specific steps are as follows: in the preprocessing stage, the image is segmented, the background is filtered out, and the image block is cut, and the coordinates are recorded; in the feature extraction and fusion stage, the pathological features are extracted through a pre-trained convolutional model, then the coordinates are normalized to form position features, and the pathological features and position features are fused to form comprehensive features with spatial information. In the reasoning stage, a multi-scale feature enhancement network is introduced, which can capture local features through convolution operations and model spatial relationships through attention mechanisms, thus achieving more comprehensive processing of local and spatial information. Finally, the parallel classifier is used to solve the problems of class imbalance and sample shortage, and the final pathological classification result is obtained. This method can better assist pathologists in improving the diagnosis accuracy and reducing subjective differences in the diagnosis of chondroma.

A method of determining a corrected path of an industrial robot and a corresponding electronic device. The method comprises obtaining a first path (S1) from a teaching device (140), wherein the teaching device (140) is actuated by an operator (160) to move along the first path (S1), and wherein an identification device (132) is provided to allow an imaging device (142) to capture the identification device (132) to determine a first relative position of the teaching device (140) relative to the identification device (132), wherein one of the identification device (132) and the imaging device (142) is mounted to the teaching device (140); obtaining a second path (S2) from the teaching device (140), wherein the teaching device (140) is mounted onto the industrial robot (110) and is actuated by the industrial robot (110) to move along the second path (S2); wherein the second path (S2) is determined based on a second relative position of the teaching device (140) relative to the industrial robot (110) and wherein the second relative position is determined based on a first relative position of the teaching device(140) relative to the identification device (132); and determining a relative spatial relationship between the first path (S1) and the second path (S2) to generate a corrected path (Sc). According to the method and the corresponding electronic device, the teaching process for the industrial robot (110) gets better accuracy and less set-up efforts.

This invention discloses a parametric sketch-driven method for the deconstruction and reassembly assembly design of metal tubular furniture, belonging to the field of digital furniture design and intelligent manufacturing technology. The method includes: decoupling the furniture structure into multiple sheet-like substructures and establishing a parametric template library; extracting primitive data and determining connection types by identifying the geometric and topological features of user-defined 2D sketches; automatically matching and driving the templates to generate 3D substructures, completing the process design of connection details; calculating assembly positioning benchmarks and contact areas based on the spatial relationships between sketches, generating connection features to achieve automatic assembly; and finally, performing dimensional verification and closed-loop updates on the assembly. The entire process requires no manual intervention and can output a production-ready digital model and processing data within minutes, significantly improving the efficiency, accuracy, and manufacturability of customized metal tubular furniture design.

The application discloses a kind of based on behavior tree and VLA's dual-arm robot task assembly method, specifically includes: when task starts, behavior tree is executed from root node, signal is sent to child node by fixed frequency;Proposition received by parsing perception module is extracted target object and its spatial relationship by conditional node, and three-dimensional information and attribute are obtained using visual recognition algorithm, to judge whether meet the requirement;Action node generates initial path according to motion planning module, trajectory optimization is carried out according to the dynamics constraint of manipulator, and deviation and disturbance in motion process are monitored in real time, timely adjustment is ensured to complete task, wherein user instruction and scene image information are integrated, joint angle and end effector instruction are generated by means of pre-trained multi-modal VLA model, guide dual-arm efficient cooperation, until successfully complete assembly operation.

This invention provides a method, apparatus, and electronic device for reconstructing a sparse pressure field on the surface of a high-speed train. The method employs a pre-trained reconstruction model, which includes a node selection network, a coordinate encoder, and a pressure field reconstruction network. The method includes: selecting multiple sampling nodes from the pressure field point cloud on the high-speed train surface using the node selection network as recommended sensor placement locations; generating a pressure field feature vector based on the selected sampling nodes and the input pressure field point cloud; acquiring the three-dimensional coordinates corresponding to the multiple sampling nodes and constructing a coordinate matrix; encoding the coordinate matrix using the coordinate encoder to extract spatial information feature vectors representing the spatial relationships between all sampling nodes; and generating a dense pressure field based on the pressure field feature vectors and the spatial information feature vectors using the pressure field reconstruction network. This invention improves the reconstruction accuracy of the pressure field on the surface of high-speed trains under complex operating conditions and enhances its multi-condition generalization capability.

The application relates to the technical field of medical image processing, in particular to a three-dimensional medical image registration method and device, electronic equipment and a storage medium. The method comprises the following steps: determining target points matched with preset reference points on a reference image from a mobile image; wherein the reference points are preset with one or more, therefore, the target points also have one or more, so that the matching of the reference points on the reference image and the target points on the mobile image can be realized; determining a global displacement amount of the mobile image according to the displacement deviation of all the target points, so that all the target points in the mobile image can be constrained, and the spatial relationship among all the target points is established; and registering the mobile image according to the global displacement amount, so that the points in the registered mobile image can meet the requirements of an anatomical structure as much as possible.

The application discloses an image super-resolution method based on omnidirectional space feature learning, a terminal and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: using an encoder network to extract an initial shallow feature map of an input low-resolution image; inputting the initial shallow feature map into a deep feature extraction network composed of a plurality of cascaded omnidirectional feature extraction modules to perform spatial relationship perception feature extraction, channel relationship perception feature extraction and multi-scale detail perception feature extraction on the initial shallow feature map, and obtaining an omnidirectional feature map; inputting the omnidirectional feature map into a high-frequency texture enhancement module to perform frequency modulation feature enhancement and sparse non-local feature extraction on the omnidirectional feature map, and generating a texture enhancement feature map; performing context-aware feature aggregation on the texture enhancement feature map to obtain an aggregated feature vector; and performing implicit decoding and image reconstruction on the aggregated feature vector to obtain a high-resolution image. The application can comprehensively capture image features, effectively restore high-frequency textures and intelligently aggregate features.

The application discloses a kind of based on GiST spatial index multi-resolution fragmentation DEM database construction method, comprising: S1, with the resolution of fragmentation DEM image tile as hierarchical division standard;S2, existing DEM image tile is hierarchically divided according to S1 hierarchical division standard, to minimum outer rectangle as unit independent storage;S3, based on DEM image tile, the hierarchical structure rule of R tree is used to establish parent-child node relationship, and the initial R tree multi-resolution skeleton is constructed;S4, on the basis of initial R tree multi-resolution skeleton, uniform different hierarchical spatial relationship description rule is formulated, and according to the rule, the spatial correlation between DEM image tile is described, and the preliminary establishment of multi-resolution DEM database based on GiST spatial index is completed;S5, the DEM image tile to be stored is stored into database, and the database is improved.The application improves the performance of DEM data storage.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

This application discloses a target tracking method based on pose recognition. By acquiring the current image frame, pose recognition is performed on the detected target to obtain the coordinates and confidence scores of multiple pose keypoints. Valid keypoints are then selected based on the confidence scores, and a pose keypoint bounding box is constructed and expanded to obtain a pose-completed bounding box. Simultaneously, the orientation angle of the detected target is calculated. Based on this, a target trajectory is established and continuously updated based on previous image frames. For each target trajectory, a predicted trajectory position and predicted orientation angle corresponding to the current image frame are generated. The matching cost is calculated by combining the spatial relationship between the predicted trajectory position and the pose-completed bounding box. When multiple candidate detected targets exist, the final matched detected target is determined based on the orientation angle difference. This application improves the matching accuracy and tracking stability in occluded and crowded scenes through pose completion and orientation angle disambiguation mechanisms.

The application belongs to the technical field of oil well fault detection, and particularly relates to an oil well sensor spatial relationship modeling method based on centrality guided graph convolution, which comprises the following steps: firstly, valid nodes are reserved through node integrity maintenance, and state and trend features are extracted by using a double-view feature generation method; secondly, a weighted hybrid adjacency matrix is constructed by fusing prior physical connection and data-driven correlation; then, based on the weighted hybrid adjacency matrix and the node centrality score, a weight parameter for guiding spatial information aggregation is generated; finally, based on the weight parameter, centrality guided graph convolution operation is performed on a two-dimensional feature vector of the sensor node to update the node feature, and spatial relationship modeling is completed. The application introduces and optimizes a physical graph topology in oil well fault detection for the first time, effectively models the complex spatial dependence between sensors, significantly improves the interpretability, accuracy and robustness of a subsequent fault detection task, and is particularly suitable for identifying long-time evolving faults.

The embodiment of the application provides a shield tunneling machine cutter head water jet cleaning test method and a shield tunneling machine, wherein, in the method, the Hungarian distribution algorithm performs line comparison, cross-line matching and minimum cost relationship establishment on a time matrix, so that a clear minimum time correlation is formed between nozzles and sub-areas, and a calculable balanced relationship of cleaning load is formed among multiple nozzles; the particle swarm optimization algorithm is used to perform fine adjustment of nozzle mounting points, so that nozzle coordinates can be continuously updated within a search range after displacement in a fixed direction; the position of a group of particles is iterated to find a higher reachability of a jet posture under distance and angle conditions, so that the spatial relationship between nozzle pointing, action area normal vector and cutter head structure is updated in multiple rounds, higher coverage matching accuracy is obtained, the nozzle start time and the time amount of the assigned sub-area are formed into a unified time axis and are aligned with a rotation reference, the nozzle start-stop behavior and the cutter head position form a continuous and recordable cleaning structure.

The present application relates to the field of industrial vision enhancement, more specifically, it relates to an underwater image enhancement method based on Transformer and multi-scale spatial domain feature fusion, the present application introduces the mechanism of Transformer, uses self-attention and linear attention mechanism to capture the complex spatial relationship in the image and improve the image quality, at the same time, the multi-scale feature convolution module is designed to extract information of different scales and levels, which can reduce the complexity of the network and improve the calculation efficiency, in addition, an air domain feature fusion module is designed to calculate the attention in the local neighborhood of pixels, thereby improving the calculation efficiency and feature fusion, the method has the advantages of good generalization ability, high robustness and high enhancement efficiency, and can be widely applied in underwater pipeline detection and maintenance, underwater environment monitoring, underwater biological research and other fields, and provides strong support for scientific research and engineering practice in related fields.

The application discloses a kind of RT-DETR-based unmanned aerial vehicle aeronautical image detection method and system.For the problem that small target in unmanned aerial vehicle aerial photography is easily disturbed by complex background and has large scale variation, the application first carries out data enhancement preprocessing on high-resolution aerial photograph image;Second, a feature extraction network is constructed by fusing CSP and convolution attention mechanism to enhance the discriminability of small target features;In the feature fusion stage, introduce P2 high-resolution detection head, lightweight adaptive feature fusion module and partial convolution CSP structure to realize efficient interaction of multi-scale features;In addition, a position relationship detection head is designed to explicitly model the target space relationship, and an improved generalized center distance loss function incorporating aspect ratio constraints is used to accelerate model convergence.Finally, the model is deployed on an edge visualization system based on Streamlit.The application effectively reduces the missed detection and false detection in dense occlusion scenarios, achieving an efficient balance between detection accuracy and real-time performance.

The application provides an agricultural intelligent question and answer method and device, electronic equipment and storage medium, and relates to the technical field of agricultural intelligence. The application retrieves a space-time knowledge graph with time relationship data and space relationship data through a search condition, obtains a space-time context, supplements the space-time change condition of an answer, enables a large language model to answer in response to the space-time change condition, and improves the space-time accuracy of the answer. The application realizes the generation of differentiated prompt sentences and answers according to regions and user layers, solves the problem of the existing agricultural intelligent answer of 'one face for thousands of people', and can serve professional users and non-professional users at the same time. The application combines space-time reasoning with the powerful generation capability of an LLM, so that the agricultural intelligent question and answer system can not only answer static fact questions, but also has a dynamic analysis and prediction capability based on space-time rules, such as disaster warning and agricultural operation suggestions, and has a more powerful function.

Methods and devices to customize a virtual keyboard on a touchscreen display. A request or other trigger event may be associated with an image that has one or more dominant colours. A set of colours is determined within colour space by varying the coordinates of the dominant colour (s) in one or more dimensions. Each colour in the set of colours is applied to one or more keys of the virtual keyboard. The colours may produce a gradient effect. The dominant colour may be determined based on object recognition and segmentation. Multiple objects may be identified and used to determine the set of colours and their relative spatial relationship may be reflected in how the colours are mapped to the virtual keyboard.

This invention relates to a direction-finding method based on matrix decomposition, belonging to the field of array signal processing technology. The invention first calculates the covariance matrix of the received data, then performs matrix decomposition and reconstruction based on the covariance matrix, and finally constructs a solution function by combining the spatial relationship between the reconstructed matrix and the array steering vector. The direction of arrival of the signal source is obtained from the mapping relationship between the peak value of the solution function and the signal incident angle. This invention utilizes the orthogonality between noise vectors to reduce the impact of noise on the direction of arrival (DOA), which can further improve the stability of the direction-finding algorithm.

An automatic detection method for CBCT cephalometric landmarks based on multi-geometric guidance and specific perceptual coding includes the following steps: Step S1, downsampling the 3D CBCT image and obtaining a preliminary coordinate set through a coarse localization network; Step S2, cropping image blocks centered on the coordinates and extracting local features through a visual encoder containing a shared basic encoder and a low-rank adapter; Step S3, calculating the relative position matrix of the landmarks, encoding spatial relationships using radial basis functions, and constructing a multi-anatomical heterogeneous map; Step S4, inputting visual and edge features into a multi-geometric guidance Transformer, fusing global constraints and updating features using an attention mechanism; Step S5, extracting directional geometric relationships using spherical harmonic functions to construct higher-order update terms, and dynamically updating the heterogeneous map using a gated residual mechanism; Step S6, predicting coordinate offsets through a multi-layer network and performing iterative optimization to output high-precision 3D coordinates. This method significantly improves detection accuracy and robustness.