95 results about "Spatial reconstruction" patented technology

The invention relates to the field of grain storage management, and discloses an intelligent granary grain pile dew point early warning method and system, and the method comprises the steps: carrying out the continuous collection of the temperature and humidity, airflow velocity and pile density of each grain pile in a granary, and generating a humidity and heat perception graph reflecting the spatial layering characteristics; performing time sliding and spatial reconstruction analysis on the damp-heat perception graph, and constructing a damp-heat evolution mapping model; based on the damp and hot evolution mapping model, calling a multi-dimensional risk identification engine to perform aggregation judgment on the micro-region with the dew point close to a critical value, and outputting a potential condensation risk set; performing joint correction on the potential condensation risk set, real-time airflow distribution, grain pile accumulation form and ventilation response delay characteristics, and performing dynamic weight regression on different grain varieties and seasonal factors to generate dynamic stability indexes reflecting risk migration and diffusion trends; and adaptively adjusting the sensing sampling density and the early warning judgment threshold according to the dynamic stability index. The method has the advantage of improving the early warning precision of storage environment management.

The invention discloses an infrared unmanned aerial vehicle target detection method based on multi-scale self-enhancement cross-layer fusion, and aims to solve the problem of difficulty in small target detection of an infrared unmanned aerial vehicle under a complex background. According to the method, a novel target detection network is constructed, and a lossless down-sampling module and a cascade asymmetric convolution module based on spatial reconstruction are designed in a backbone network of the novel target detection network, so that multi-scale context features are extracted while information loss is reduced. The neck network adopts an enhanced pyramid structure, deep semantics and shallow details are integrated through a cross-layer feature fusion module, and a multi-scale adaptive channel attention module is introduced to dynamically re-calibrate fusion features so as to focus key information. The network adopts four detection heads to output multi-scale prediction, and uses an EIoU loss function to optimize small target positioning precision. The method can effectively improve the feature extraction and detection capability of'weak, small and dark 'infrared unmanned aerial vehicle targets, and has higher robustness and accuracy in a complex scene.

The invention relates to the technical field of bridge health monitoring, and discloses a method for monitoring the bearing capacity state of a bridge latticed column structure. The method comprises the following steps: constructing an original monitoring field according to a sensor time sequence signal, and generating a state evolution sequence representing the overall dynamic evolution of the bridge latticed column through multi-dimensional space reconstruction, so as to capture the nonlinear characteristics of the structure behavior and provide a basis for anomaly detection. And positioning a potential abnormal region based on the deviation degree from the reference model, and extracting a time-varying characteristic spectrum of the potential abnormal region to drive the digital twin model to perform simulation so as to obtain a simulation response spectrum. The simulation response spectrum and the actual measurement state evolution sequence are subjected to space-time fusion, a mixed state field fusing real data and physical mechanism inference is generated, a monitoring blind area is made up, and the data reliability is improved. And by performing multi-scale decomposition on the mixed state field, the specific state mode of the bearing capacity of the bridge latticed column is identified, and more accurate and more complete evaluation of the bearing capacity abnormity is realized.

The invention provides an atmospheric environment particulate matter pollution detection method and device, and the method comprises the steps: carrying out the grading analysis of the scattered light intensity at each environment detection point in an environment pollution detection region, and obtaining the particle size distribution characteristics of air particulate matters at each environment detection point in a plurality of particle size intervals; determining a spatial distribution field of the particulate matter concentration in the air based on the particle size distribution characteristics; determining a dynamic correction factor of diffusion and aggregation of particulate matters in the air according to an atmospheric physical state, and further performing dynamic correction on the spatial distribution field based on the dynamic correction factor; performing spatial reconstruction on the spatial distribution field obtained by dynamic correction on a regular grid, and determining the distribution pattern of the air particulate matter concentration in the environmental pollution detection area according to a reconstruction result; and generating a particulate matter pollution index in the environmental pollution detection area according to the distribution pattern. By adopting the scheme of the invention, the reliability of atmospheric environment particulate matter pollution detection can be improved based on particulate matter concentration space reconstruction of multi-point dynamic correction.

The invention discloses an equipment surface defect detection method and system based on three-dimensional space reconstruction. The method comprises the following steps: acquiring multi-view spatial data of standard equipment and equipment to be detected, and respectively constructing three-dimensional models containing geometric structures and surface features; registering the three-dimensional point cloud data of the standard equipment and the to-be-detected equipment into the same world coordinate system, and automatically generating two-dimensional rendering pictures of the standard equipment and the to-be-detected equipment with the same visual angle and pose in the same coordinate system; and further performing surface defect detection on the two-dimensional rendering pictures corresponding to the two-dimensional rendering pictures, and visually presenting a defect detection result at a corresponding position of the three-dimensional model. According to the invention, artificial leak detection and error detection are effectively avoided, and the efficiency and accuracy of equipment surface defect detection are remarkably improved.

The invention discloses an underground pipeline detection method and system based on image recognition. The method comprises the following steps: obtaining a preprocessed RGB image sequence and registered laser radar point cloud data; generating a target detection frame set with semantic tags; segmenting the obtained initial two-dimensional semantics into a mask set; generating a two-dimensional semantic segmentation mask set after depth calibration and a semantic point cloud cluster set after depth calibration; obtaining a two-dimensional semantic segmentation mask set after cylinder verification and a semantic point cloud cluster set after cylinder verification; outputting a two-way binding semantic point cloud cluster set; and spatial reconstruction processing is executed based on the two-way bound semantic point cloud cluster set, and underground pipeline spatial recognition detection is completed. According to the method, three-dimensional geometric consistency is introduced as a verification condition on an algorithm structure, so that a segmentation result simultaneously meets semantic rationality and spatial physical authenticity.

The invention discloses a salinized frozen soil roadbed settlement prediction method and system, and relates to the technical field of roadbed settlement prediction, and the method comprises the steps: obtaining multi-source monitoring data of a target salinized frozen soil roadbed, the multi-source monitoring data comprises temperature, moisture content, conductivity, stress and ground surface settlement time sequence observation values distributed along the depth, and a ground surface remote sensing image; establishing a physical mechanism model reflecting the heat-water-salt-force coupling effect of the salted frozen soil based on the multi-source monitoring data; integrating the physical mechanism model as a constraint condition into a neural network training process, and constructing a settlement response prediction sub-model with physical consistency in combination with multi-source monitoring data; performing spatial reconstruction on the multi-source monitoring data to form a two-dimensional profile image containing a temperature field, a humidity field and a salt field, and inputting the two-dimensional profile image into a convolutional neural network to extract spatial structure features; and constructing graph structure data by utilizing spatial structure characteristics, modeling a mutual influence relationship among different salinized areas through a graph neural network, and outputting settlement tendency distribution.

The invention relates to the technical field of defect detection imaging, and discloses a high-precision three-dimensional imaging method and system based on continuous magnetic flux leakage data. Collecting surface magnetic flux leakage signals of a measured target to form an initial signal set, and extracting feature vectors by using a convolutional neural network model; separating three-dimensional space data, and obtaining initial three-dimensional position coordinates of the defect through an iterative mapping algorithm; mining spatial geometric features, determining potential geometric boundaries, and fusing data to construct a refined position model; extracting a detection subset, correcting a shape error, constructing a space reconstruction grid, filling holes, and obtaining a complete three-dimensional shape representation; and through precision calibration and signal matching verification, a high-precision three-dimensional image is output. According to the method, the defect positioning and shape reconstruction precision can be effectively improved, the imaging integrity and accuracy are guaranteed, the problems of fuzzy positioning and shape distortion of traditional magnetic flux leakage imaging are solved, and the defect detection requirements of a complex detected target are met.

The invention provides a sedimentary strata three-dimensional geology construction method and system integrating carbon footprint evaluation and intelligent block modeling, and relates to the field of energy and geology. According to the method, carbon footprint evaluation and sedimentary stratum three-dimensional geological construction are organically fused, so that full-process integration of data acquisition, dynamic partitioning, spatial reconstruction and low-carbon scheduling is realized, and the bottlenecks that data and carbon emission information are separated and model precision and energy consumption control are difficult to balance in the traditional technology are broken through; the real-time performance of fine expression of geological structures and environmental risk assessment is greatly improved, and more quantitative and intelligent technical support is provided for carbon capture site selection and green exploration decision.

This application relates to a robotic method for precise ore sampling in complex mining environments, specifically in the field of ore feature recognition. The method includes: acquiring LiDAR point cloud data and industrial camera image data; after voxelizing the point cloud data, obtaining LiDAR BEV features through an improved SPConv sparse pyramid convolution operator feature extraction network, spatial reconstruction unit, and channel reconstruction unit; extracting semantic features from the image data to generate an image BEV feature map; fusing the two data elements-wise in the BEV space; extracting multi-scale features from the fused BEV feature map using a dual-branch Decoder network; introducing a KA attention mechanism to weight the fused features; and calculating a sampling priority score based on the weighted feature map to determine the optimal sampling point and plan the path. This method can improve ore recognition accuracy in complex mining environments and shorten the decision-making time for the entire process of sampling location determination and path planning.

A machine-oriented video coding (VCM) decoding apparatus is proposed in one example of the disclosure. The VCM decoding apparatus includes an internal decoder that generates a feature map by decoding a bitstream and parses information about the feature map by decoding the bitstream, and an image reconstructor that reconstructs at least one skipped channel in the decoded feature map based on the information about the feature map, wherein the information about the feature map can include at least one of the following: group-wise coding information including whether each channel of the feature map is skipped, a quantization compensation parameter, feature map truncation information, temporal reconstruction information, spatial reconstruction information, and packing information.

The invention relates to the technical field of image fusion, in particular to a cross-spectrum fusion and identification optimization method under the condition of low infrared contrast at night. According to the method, on the basis of edge strength of coordinate points on an infrared image, motion consistency is represented on the basis of motion information embodied on the infrared image, and a confidence coefficient weight is solved for each coordinate point in combination with a specific brightness attribute. Based on a matching process, an initial geometric correction displacement field can be constructed for an optimal matching point in a visible light image through a reference point in an infrared image, and filtering operation is performed in vertical and horizontal directions to obtain a smooth geometric correction displacement field for spatial reconstruction. The feature information is screened through weighted fusion of the spatial correction thermal imaging image after spatial reconstruction, a fused image is obtained, and accurate and effective target recognition can be achieved based on the fused image. According to the invention, through effective image fusion, the accuracy of night target identification is improved.

This invention discloses a method for spatial sampling and reconstruction of ozone concentration distribution in a dielectric barrier discharge device, belonging to the field of ozone concentration measurement and image reconstruction technology. The method includes: setting up a sampling area in the vicinity of the discharge device and defining orthogonal sets of first and second measurement lines; measuring the measurement lines using an absorption spectroscopy measurement device to obtain two sets of orthogonal one-dimensional projection measurement data; controlling the sampling sequence through a stability criterion after switching measurement lines; performing windowing processing and frequency domain analysis on the projection sequence to obtain spectral information; constructing a two-dimensional frequency domain representation and applying physical prior constraints; performing a two-dimensional inverse transform to obtain initial values ​​of the lattice, and outputting a continuous distribution result after spatial reconstruction and refinement. This invention can achieve reliable retrospective reconstruction of the ozone concentration field under conditions of limited projection measurement data and asynchronous measurements, providing a basis for spatial distribution diagnosis and performance evaluation of the ozone generation process in dielectric barrier discharge.

The application provides a method, device and equipment for evaluating spatial thermal effect under heat exposure, and relates to the technical field of geographic environment data processing. The method comprises the following steps: acquiring green infrastructure patches in a target region; performing spatial feature analysis on the green infrastructure patches to obtain a cooling supply index under different scenarios; acquiring population data and thermal environment data in the target region, obtaining a target model according to the population data and the thermal environment data, and obtaining population data with a second spatial resolution through the target model; obtaining a thermal risk index under different scenarios according to the population data with the second spatial resolution and surface temperature data; and evaluating the heat balance state of the target region according to the cooling supply index and the thermal risk index, identifying a spatial region of a target category, and obtaining a spatial thermal effect evaluation result. The scheme of the application evaluates the heat balance state of the target region, quantifies regional losses caused by high temperature, and provides targeted spatial reconstruction strategies.

The application discloses a ship cabin three-dimensional modeling method and system based on BIM and laser scanning cooperation, and relates to the technical field of three-dimensional digital modeling, and comprises the following steps: S100, panoramic observation is carried out on the cabin interior of a ship, the geometric edges of the cabin point cloud sparse band are extracted, a continuous density contour is generated, and the contour is used as a baseline for space reconstruction; S200, continuous anchor strips are arranged at the positions of the geometric edges according to the density contour, the true boundary trend is formed by extending along the direction of the density contour, and the unified control of the extension direction of the bulkhead is realized. Through the anchor strip guidance, the forbidden supplement isolation, the attitude oblique cutting supplement and the dynamic curtain laying mechanism, the true boundary is accurately identified, the virtual topology misjudgment is avoided, the geometric precision and the semantic reliability of modeling are improved, and the risk of structure deviation and interference is avoided.

The application provides a spatial reconstruction method for residual stress distribution in an epoxy composite insulating part. According to the test result of ultrasonic longitudinal wave speed in the test piece, the shrinkage rate calculation meeting the speed distribution is realized through an inversion algorithm and an elasticity theory, and the residual stress distribution in the test piece is constructed. The method can realize efficient evaluation of the residual stress in the test piece. The inversion method for calculating the internal stress of the material through the ultrasonic speed is first proposed, and the residual stress distribution in the test piece can be effectively evaluated.

The invention relates to the field of big data analysis, and discloses a wind field data analysis system and method oriented to runway running direction optimization, and the method comprises the steps: carrying out the real-time block disassembly processing of an obtained high-speed meteorological record, and forming a wind field basic record set with scene specific time sequence deviation information; cross-position mutual verification operation is applied to the wind field basic record set, and multi-source wind field expression reflecting local airflow fine offset is generated; according to the multi-source wind field expression, key airflow conduction veins in the runway extension direction are identified by utilizing an interlayer screening and judging strategy, and a small-scale wind field disturbance form only appearing in a runway scene is locked; the small-scale wind field disturbance form is split again, and a local wind field composite unit reflecting the combined action of the space reconstruction error and the wind trend deflection in the runway direction is generated; and deducing a wind field instant judgment chain suitable for updating the running direction of the runway through a local wind field composite unit. The method has the advantage that the real-time performance of runway running direction optimization is improved.

The invention discloses a heat map sequence downsampling reconstruction method based on tensor space constraint, and relates to the field of photo-thermal science and detection and signal processing. The invention aims to solve the problem that an active infrared thermal wave imaging detection technology cannot meet the requirements of high temporal-spatial resolution, low data volume transmission and real-time monitoring in engineering application. According to the method, the core tensor of the infrared image sequence is obtained through Tucker decomposition and is updated until the convergence condition is met, and the infrared image sequence is reconstructed through the updated core tensor; the updating process comprises the following steps of: performing time mode expansion on a core tensor of an infrared image sequence, and obtaining a spatial reconstruction field under each time mode; applying thermal diffusion physical constraint on the spatial reconstruction field by adopting a lattice Boltzmann method to obtain a physical evolution field; and projecting the difference between the physical evolution field and the corresponding space reconstruction field to the corresponding position of the core tensor, and updating the core tensor slice through Adam optimization so as to update the core tensor.

The invention discloses an infrared image small target detection method and system based on boundary perception and a multi-stage aggregation network, and relates to the technical field of infrared image target detection. The objective of the invention is to solve the problems of feature loss and difficult positioning caused by low contrast of an infrared small target and a complex background. The method comprises the following steps: (1) constructing a multi-stage aggregation module; (2) constructing a spatial recombination sampling module; (3) constructing a spatial perception enhancement module; (4) establishing a boundary sensing and multi-stage aggregation network model; (5) training the network by using the existing data; and (6) detecting the infrared image by using the trained model. Compared with the prior art, the method has the advantages that small target details can be more effectively reserved under the conditions of low contrast and complex background, background noise is inhibited, and the detection accuracy and the positioning precision are effectively improved.

The embodiment of the invention provides a peanut seed measurement method and device based on three-dimensional modeling, and the privacy of data processing is ensured through innovatively designing an image pyramid structure and through multi-scale decomposition and shape constraint. A multi-view registration model is constructed, and a reliable three-dimensional reconstruction system is established in combination with projection distortion correction. Inter-layer depth mapping is introduced, and through point cloud indexing and continuity correction, a precise three-dimensional model is provided while the security of measurement data is protected. According to the method, the defects of the traditional technology in the aspects of image processing, spatial reconstruction, data management and the like are effectively overcome.

A device generating a reconstructed 3D mesh from a single image is provided. According to one embodiment of the present disclosure, a device may comprise a memory configured to store a depth refinement model; and a processor configured to: acquire a single image representing an indoor space, generate an initial depth map from the single image, generate a plurality of sampling data from the single image, generate a plurality of depth maps from the initial depth map and the plurality of sampling data through the depth refinement model, calculate a plurality of losses representing differences between each of the plurality of depth maps and a correct depth map, and train the depth refinement model such that a sum of the calculated plurality of losses is minimized.

The invention discloses a comprehensive online intelligent monitoring method and device for a high-voltage cable, and relates to the technical field of online monitoring. The method comprises the following steps: collecting static and dynamic and environmental data of a high-voltage cable, and generating a digital twin model through 3D modeling and electrothermal field coupling; normal working condition parameters are screened and input into the model for calculation to obtain a standard baseline; real-time data is acquired, reconstructed and compared with a standard base line, and temperature and electric field abnormal sections are determined; inputting preset parameters to deduce the physical quantity change of the abnormal section, and outputting a potential risk trajectory; and finding out heat and electricity dominated failure points according to judgment conditions, extrapolating residual life by using corresponding models, and calculating a failure probability through a Monte Carlo algorithm to complete online monitoring. According to the method, the deviation is calculated through real-time data acquisition and processing, spatial reconstruction and standard baseline comparison, so that the abnormal condition of the cable is detected timely and accurately.

This application provides a coal flow control method, device, and coal flow monitoring system. The method includes: receiving three-dimensional coal flow point cloud data sent by a 3D camera, and processing the three-dimensional coal flow point cloud data based on three-dimensional spatial reconstruction technology to obtain coal flow spatial data; performing integral processing on the coal flow spatial data in the time domain to obtain the coal flow rate per unit time, and processing the coal flow spatial data using a large coal identification model to obtain the large coal grade; determining the load level of the scraper conveyor based on the large coal grade and the coal flow rate range within the unit time; and adjusting the conveying speed of the scraper conveyor to the speed corresponding to the load level. This improves the accuracy of coal flow monitoring and solves the problem of low accuracy in existing coal flow monitoring methods.

A system may include a lateral shearing interferometer. The lateral shearing interferometer may enable measuring a surface profile of a sample, such as the pattern wafers, using reflected light from the sample. The reflected light may be reflected at a grazing incidence angle. A controller may receive images from the lateral shearing interferometer and measure the surface profile. The controller may measure the surface profile by reconstructing a phase of the reflected light from the images. The controller may reconstruct the phase using spatial reconstruction or time-domain reconstruction. The gratings may allow the controller to measure the edge roll-off in one or two dimensions. The controller may also calibrate the grazing incidence angle and determine refractive indices of thin-films on the sample. The controller may also perform full measurements of the surface profile using line beams, rectangular beams, or full-sample beams.

The invention relates to a method, a corresponding device and a medical technology system in the form of an MRI system and / or a diagnostic system for reconstructing images in magnetic resonance imaging from provided raw data in the form of asymmetrically acquired k-space data, wherein the raw data have a symmetric and an asymmetric part, the method comprising the steps: - Reconstructing a phase image using an image space transformation of k-space data from the symmetric part of the raw data, - Applying an iterative k-space reconstruction method, starting with a working image reconstructed from the raw data to generate a new working image using the phase image, and including forming a working space through a k-space transformation of the working image, - Applying an image reconstruction method to the workspace, providing a weighting filter which, if the workspace corresponds to the raw data, has a weighting of zero wherever no raw data has been recorded, and has exclusively non-zero weightings for all other workspaces, and which has a lower weighting with respect to the raw data recorded symmetrically in k-space than with respect to the raw data recorded asymmetrically in k-space, and generating a complex intermediate image by means of an image space transformation from the k-space data weighted according to the weighting filter, which is phase-corrected with the phase image, and generating a result image as the real part of the phase-corrected intermediate image.

The invention provides a fire detection method based on deformable frequency domain reconstruction convolution, and belongs to the technical field of intelligent security and protection and artificial intelligence. According to the method, flame features are decomposed into high-frequency components and low-frequency components through a deformable frequency domain decomposition mechanism, the high-frequency components accurately capture flame flicker, edge details and other tiny features, the low-frequency components perceive the overall form of the fire behavior and smoke diffusion, and the space self-adaptive capacity of deformable convolution is combined, so that the smoke diffusion of the fire behavior is improved. The irregular shape change of the flame is dynamically fitted, and the accuracy and robustness of fire detection in a complex scene are improved. And then the high-frequency component and the low-frequency component are respectively subjected to spatial reconstruction and channel reconstruction to remove spatial redundancy, frequency domain redundancy and channel redundancy, so that the parameter quantity and the calculation complexity of the model are remarkably reduced, and the real-time detection speed of the model is effectively improved. The method has the advantages of self-adaptive frequency selection, high adaptability, improvement of the detection efficiency and reduction of the system cost.

The invention relates to a multi-data fusion muon imaging detection method and system, and belongs to the technical field of blast furnace detection imaging. The method comprises the following steps: collecting muon trajectory data penetrating through a blast furnace through muon detectors arranged around the blast furnace; spatial reconstruction is carried out based on the muon trajectory data, and a three-dimensional imaging model representing the internal density distribution of the blast furnace is constructed; obtaining multi-source observation data; wherein the multi-source observation data at least comprises thermal data and acoustic data; constructing a joint inversion objective function based on the multi-source observation data and the three-dimensional imaging model; solving the joint inversion objective function, and outputting state imaging distribution based on blast furnace three-dimensional imaging; wherein the state imaging distribution comprises material density distribution and temperature field distribution. By means of the mode, full-dimension and high-precision blast furnace internal state parameters can be obtained, and the defects that a traditional sensor is incomplete in single parameter coverage and insufficient in detection precision are overcome.

This invention provides a method for three-dimensional spatial reconstruction of weld defects based on multi-view ray imaging, belonging to the technical field of three-dimensional modeling. The method includes: establishing a ray geometry model and uniformly calibrating multi-view ray imaging parameters to obtain a multi-view projection data set; preprocessing and spatial remapping to form a linearized projection dataset, merging these datasets to construct attenuation coefficient volume data; extracting a set of porosity voxels from the volume data using a density threshold model and three-dimensional connectivity analysis; constructing a connected network model and a migration path model; and finally, spatially fusing these models with the weld geometry model to generate and output a three-dimensional spatial model of the defects. This invention can improve the accuracy of three-dimensional reconstruction of weld defects in ultra-thick plate multi-layer narrow-gap welded structures.

The application discloses a remote sensing image blind super-resolution method and system based on unsupervised learning and belongs to the technical field of remote sensing image reconstruction. The application is applied to a remote sensing image super-resolution scene in which a degradation model is unknown. First, spatial reconstruction information is transferred from a natural image field to a remote sensing image field by using transfer learning. Then, the spatial-spectral reconstruction information is optimized based on unsupervised learning. By using the transfer learning, the application does not need to be trained on remote sensing images with limited training data, and the advantages of data-driven are explicitly used. By using the unsupervised learning, the application can be generalized to remote sensing image super-resolution reconstruction tasks in various degradation scenes, the spectral reconstruction information is fully learned, and the blind super-resolution precision of the remote sensing image is improved.

The invention discloses an intelligent production scheduling method and device for a production workshop. The method comprises the following steps: establishing a mixed integer linear programming model for performing workpiece scheduling on the production workshop; performing initial solution space construction, and generating an initial scheduling solution set corresponding to the target function; performing neighborhood solution space search, solution space destruction and solution space reconstruction, and constructing a reconstruction solution corresponding to the initial scheduling solution set; replacing the initial scheduling solution set with the reconstruction solution, and repeating the steps of neighborhood solution space search, solution space destruction and solution space reconstruction until the total processing duration of all workpieces corresponding to the target function of the obtained reconstruction solution is lower than a preset value; and carrying out model decoding operation on the reconstruction solution of which the total processing duration of all workpieces corresponding to the target function is lower than a preset numerical value in combination with the coding structure to obtain a production scheduling scheme of the production workshop. The method is used for effectively optimizing the production schedule of the production workshop, reducing the maximum completion time of production and improving the production efficiency.