28 results about "Geometric distance" patented technology

The present application relates to the technical field of fusion design, in particular to a three-period minimal surface spatial positioning fusion structure design method based on the inverse distance weighting method, in the present application, by setting the implicit function equation of the three-period minimal surface and screening the control points according to the spatial distribution and geometric constraints, the accurate selection and positioning of the structure unit in the design target area are realized, the accuracy of geometric mapping is improved, the inverse distance weighting calculation is carried out through the convolutional neural network, the difference of the geometric region in the space can be effectively identified, the response ability of structure fusion is improved, the weight is calculated combined with the spatial distribution of the control points, and the weight distribution mechanism is formed by using the actual geometric distance between the control points, the continuity and smoothness of the geometric response in the interpolation region are guaranteed, the graph neural network is introduced to process the surface continuity problem, the local geometric mutation area is identified and corrected through the edge weight and topological structure between nodes, and the geometric smoothness and boundary consistency of the whole structure are improved.

The invention discloses a joint correction method and system for GNSS (Global Navigation Satellite System) non-structural deformation and a storage medium. According to the method, GNSS pseudo-range and carrier phase observation data and precise products such as a precise orbit and clock correction are obtained, a three-component displacement time sequence caused by a non-tidal atmospheric load, a non-tidal ocean load and a land hydrological load and a temperature-driven thermoelastic displacement time sequence are constructed, and the three-component displacement time sequence and the temperature-driven thermoelastic displacement time sequence are unified to a reference frame and a time system consistent with GNSS calculation. After interpolating the non-structural displacement vector to an observation epoch according to time, converting the non-structural displacement vector to a geocentric rectangular coordinate system from a site local coordinate system ENU, and projecting the non-structural displacement vector to a sight line direction from a satellite to an observation station reference point to form an equivalent geometric distance correction item; the correction terms are respectively applied to pseudo-range observation and carrier phase equivalent meter domain observation in the form of the same domain as observation, and then GNSS daily arc precise calculation is performed to output a coordinate time sequence, and preferably, the method can be used for a precise single-point positioning integer ambiguity fixing technology.

The invention discloses an underground structure scanning method for a controlled active source. According to the method, a known signal generated by an active source is equivalent to a record of a virtual station, the virtual station and an actual six-component seismograph station form a station pair, and the phase velocity of a wave under the distance is estimated through a phase difference / travel time difference between the virtual station and the actual six-component seismograph station and a geometric distance relationship between the source and the station; the obtained phase velocity is actually equivalent to a frequency dispersion curve at the midpoint of the connecting line of the source and the station, so that the phase velocity-depth structure from the source to the midpoint of the station can be obtained by performing inversion by using the obtained frequency dispersion curve; as the active source moves in the measurement area, a group of signals can be generated at different positions respectively, and a series of virtual midpoints are formed for the single-station six-component seismograph, so that the density of measurement points is remarkably increased in space. On the basis, through combination with an inversion result of a single station, three-dimensional underground velocity structure modeling with high credibility and high spatial resolution can be realized.

A method of determining device locations includes receiving a set of candidate locations in an environment and generating training data representing synthetic distance measurements between pairs of the candidate locations. The synthetic distance measurements are generated by applying random variations to the geometric distances between the candidate locations. The training data and candidate locations are used to train a machine learning model. A set of measured distances between devices are input to the trained learning model, which is used to determine a respective location in the environment of each of the devices.

The invention discloses a ground-air collaborative mapping method based on semantic feature and ground plane multi-constraint fusion, which comprises the following steps: generating a sub-map set through local point cloud data collected by a fire-fighting unmanned vehicle and an unmanned aerial vehicle, and generating a rotation-invariant semantic descriptor by utilizing a maximum height value rasterization strategy and rotation group enhancement; by calculating a geometric distance and a semantic distance and combining isomorphic and heterogeneous mode weighted fusion, loopback candidate sub-map pairs are screened, geometric registration is carried out, and loopback constraints are generated. Meanwhile, ground point cloud is extracted through an RANSAC algorithm, a sliding window smoothly updates a ground plane model, and height constraint and attitude alignment constraint are constructed. And the loopback constraint, the ground plane constraint and the pose information are input into a Ceres optimization framework for joint optimization, a globally consistent total map is generated, the precision and robustness of ground-air collaborative mapping are effectively improved, and the method is suitable for emergency scenes such as complex fire rescue and the like.

The application discloses a multi-constellation multi-fault detection and elimination method and device and electronic equipment, and belongs to the technical field of satellite navigation and positioning. The method comprises the following steps: acquiring GNSS observation data and a fisheye image, performing semantic segmentation on the image to identify a sky area, and judging satellite visibility; constructing an Euclidean distance matrix, a geometric angle matrix and an inter-satellite pseudo-range difference matrix for each constellation; based on a triangular geometric relationship and a cosine theorem, a geometric distance from a reference star to a terminal is obtained by using median robust estimation, and a residual error matrix is constructed; a fault voting matrix is generated through K-means clustering, a fault satellite set is output, and positioning calculation is performed by using non-fault satellites. The application fuses visual semantic information and inter-satellite geometric constraints, does not need iterative search and a large amount of training data, realizes one-time multi-fault real-time detection and elimination in a complex urban environment, and has good generalization ability and robustness.

A distance-based EKF positioning method, system, device and medium, comprising: collecting inertial measurement unit information, three-dimensional laser scanning points and three-dimensional point cloud map information; finding corresponding points of three-dimensional laser scanning points in three-dimensional point cloud map to realize data association; after data association, solving the distance between corresponding points to obtain geometric distance measurement and robust distance measurement; predicting the pose state at the current time according to the inertial measurement unit information; updating the pose state at the current time by using the geometric distance measurement and the robust distance measurement, and then outputting the result. The application discloses a distance-based EKF multi-sensor fusion positioning scheme, which takes the distance between three-dimensional laser scanning points and corresponding points of a three-dimensional point cloud map as the observation of a filter, realizes corresponding point finding through a k-NN fast nearest neighbor search algorithm, does not need to perform feature extraction, and does not need to perform an iterative calculation step, so that the method has great advantages in calculation efficiency compared with existing methods.

The invention relates to the technical field of global navigation satellite system data processing, and discloses a GARCH-based GNSS cycle slip detection threshold real-time determination method, which comprises the following steps: acquiring GNSS double-frequency observation data, constructing a Morse-Wiberner and geometric distance-free combination observation value, and carrying out differential processing to obtain a noise time sequence. Secondly, traversal calculation is carried out based on an akaike information criterion, and the optimal order and the sliding window length of the GARCH model are determined; then, a current epoch condition standard deviation is predicted using the model, and a multiple factor is adjusted in combination with an environmental state to construct a dynamic detection threshold. And finally, comparing an absolute value of the noise sequence with a threshold value to judge a cycle slip, and calculating an integer solution by constructing an observation equation to complete repair. According to the method, the heterovariance characteristic of the observation noise is modeled by using the GARCH model, so that the adaptive adjustment of the detection threshold along with the environmental noise level is realized, the misjudgment rate in a complex environment is effectively reduced, and the detection accuracy is improved.

The application discloses a joint correction method and system of GNSS non-tectonic deformation and a storage medium. The method obtains GNSS pseudo-range and carrier phase observation data and precise orbit, clock error and other precise products, constructs three-component displacement time series caused by non-tidal atmospheric load, non-tidal ocean load and land hydrology load and temperature-driven thermal elastic displacement time series, and unifies to a reference frame and a time system consistent with GNSS solution. The non-tectonic displacement vector is interpolated to the observation epoch by time, converted from the local coordinate system ENU to the geocentric rectangular coordinate system, and projected to the line-of-sight direction of the satellite to the reference point of the station to form an equivalent geometric distance correction term; the correction term is applied to the pseudo-range observation and the carrier phase equivalent meter domain observation in the same domain as the observation, and then GNSS daily arc segment precise solution is performed to output the coordinate time sequence, which is preferably used for precise point positioning integer ambiguity fixing technology.

The invention discloses a cloth simulation method, device and equipment and a computer readable storage medium. The cloth simulation method comprises the steps that collision detection is conducted on finite element units through a linear hierarchical bounding box collision detection algorithm; calculating the minimum geometric distance corresponding to each detected collision candidate pair; determining total collision potential energy according to each minimum geometric distance, a preset global rigidity coefficient and a preset minimum safety gap distance by utilizing a local obstacle function; determining local friction potential energy respectively corresponding to each collision candidate pair according to the relative position vector and the normal contact force scalar quantity respectively corresponding to each collision candidate pair, and determining total friction potential energy; according to the boundary stiffness coefficient, the augmented Lagrangian multiplier and the target positions corresponding to the vertexes of the finite element units, local boundary potential energy corresponding to the vertexes of the finite element units is determined, and total boundary potential energy is determined; and performing cloth simulation according to the total collision potential energy, the total friction potential energy and the total boundary potential energy. According to the invention, penetration between the cloth and the cloth or an external object is avoided.

The invention discloses a three-dimensional constellation shaping method based on regional probability folding and weighted neighborhood mapping, which belongs to the technical field of communication, and comprises the following steps of: calculating a radial distance of a constellation point coordinate, performing hierarchical division to obtain a radial energy hierarchical set, and calculating an effective connectivity degree of the constellation point by identifying a primary neighbor and a secondary neighbor to obtain an effective connectivity degree of the constellation point; carrying out folding frequency distribution based on a hierarchical scale factor to obtain folding frequency and non-uniform probability distribution of each constellation point, and constructing a probability-geometry joint weighted edge weight based on a geometric distance between the constellation points to obtain a weighted neighborhood matrix; according to the weighted neighborhood matrix and the bit tag Hamming distance, a global mapping cost function is established, and an optimal bit mapping relation is obtained for three-dimensional constellation shaping. The method solves the problems that in the prior art, probability distribution only depends on the energy criterion, but the local topology difference of constellation points on the same layer is ignored, so that the error code risk is unbalanced; and system performance limitation caused by mismatching of bit mapping and probability distribution under a non-uniform probability condition is solved.

Techniques and systems are provided for generating depth information for an image. For instance, a process can include obtaining one or more images of an environment. The process can further include generating a set of features for the one or more images. The process can also include combining the set of features with one or more distance maps to generate combined feature distance information, wherein the one or more distance maps indicate distances based on relative height above a ground level. The process can further include generating depth information of the environment based on the combined feature distance information, and outputting the depth information of the environment.

This application discloses a real-time decision-making method, system, and medium for intelligent agents based on Riemannian manifolds, relating to the field of artificial intelligence technology. The method, executed by an intelligent decision-making system, includes: constructing a behavior transition graph based on a historical trajectory dataset; mapping the attention matrix generated by a large language model in the decision-making task to a symmetric positive definite matrix manifold for representation; embedding the behavior transition graph into a hyperbolic Riemannian space for representation; learning the geometric alignment mapping from the symmetric positive definite matrix manifold and hyperbolic Riemannian space to a common metric space by optimizing the objective loss function; in the inference phase, acquiring current environmental observation data and inputting it into the large language model to obtain the corresponding attention representation, which is then mapped to the common metric space to obtain a query vector; and using actions corresponding to action nodes with a geometric distance below a preset threshold as control commands. This significantly reduces the computational overhead of decision-making and improves cross-scenario generalization ability and decision interpretability.

The invention relates to path planning, in particular to an autonomous search path planning method and system for a rescue robot, and specifically, the method comprises the following steps: obtaining a grid map of a rescue environment, and calculating a weighted extension distance fusing a geometric distance, terrain trafficability and environment risk cost for adjacent nodes; a forward open list starting from a starting point and a reverse open list starting from a target are established, and in bidirectional search, environmental risks are included in a forward evaluation function, and the environmental risks are not included in a reverse evaluation function; when a node accessed in the other direction is searched in a certain direction, recording the node as an initial intersection, and continuing bidirectional expansion by taking the total expansion distance of the current path as an upper bound to search a better intersection; in all the intersection points, selecting a point which enables the total extension distance to be minimum after the forward path and the reverse path are spliced as an optimal intersection point, and generating an initial path; and smoothing the initial path to ensure that the terrain trafficability and the environmental risk value of each point on the smoothed path are within a safety threshold, and obtaining a feasible path.

The invention provides a space intelligent path generation method and device based on space function topology and a medium, belongs to the technical field of space intelligence, and can enable the space intelligent navigation and path planning technology not only to depend on geometric distance, but also to model space function semantics and topology logic constraints. The space function topology-based space intelligent path generation method comprises the following steps: obtaining structure information of a space environment to construct a topological graph, the structure information comprising space structure information and function region information; performing information propagation and aggregation on nodes of the topological graph by using a graph neural network to generate a global topological embedding matrix; and based on the global topology embedding matrix, selecting an optimal path between the nodes, so that the optimal path can be reached in a physical space and logic constraints are met. The invention discloses an intelligent spatial path generation device based on spatial functional topology and a computer storage medium.

This invention proposes a remote sensing rotating target detection algorithm based on dynamic orientation-aware attention. First, addressing the problem of insufficient multi-scale feature fusion, a cross-scale dense fusion feature pyramid network is proposed. Through full-scale dense connections and feature distillation mechanisms, the deep fusion of semantic and detailed information is enhanced. Second, addressing the problem of insufficient target orientation awareness, a dynamic orientation-aware attention module is proposed. This module dynamically generates rotating convolutional kernels guided by angle information, achieving alignment between the receptive field and the target orientation. Finally, to address the training instability caused by angle periodicity, a Gaussian weighted chamfered distance loss is constructed, transforming angle error into a geometric distance penalty. An angle-aware Gaussian weight is introduced to alleviate angle discontinuities. This invention effectively solves the problems of insufficient multi-scale feature fusion, insufficient orientation awareness, and training instability caused by angle periodicity in remote sensing rotating target detection.

The invention relates to the field of generative artificial intelligence. The invention provides a user interface code generation method and system and a medium. The user interface code generation method comprises the steps of obtaining user interface screenshot data to extract an atomic component set and a text element set; constructing and solving a weighted bipartite graph based on the atomic component set and the text element set so as to merge the successfully matched atomic components and the text into a composite atomic component, and reserving the unsuccessfully matched atomic components and the text as independent atomic components, the weighted bipartite graph realizes semantic matching of the atomic component and the text according to the geometric distance, the alignment direction and the semantic correlation; performing structural analysis on the atomic component, wherein the structural analysis comprises the steps of generating a strong constraint container by adopting a differential aggregation strategy and generating a pre-mask area based on the strong constraint container; constructing a full-page component tree by using an improved recursive projection segmentation algorithm under the protection mechanism of the pre-mask area; and traversing the full-page component tree by adopting a post-order traversal strategy to generate codes in a divide-and-conquer manner.

The invention provides a three-dimensional point cloud denoising method and device, equipment and a storage medium, and the method comprises the steps: carrying out the local normal estimation of each point in a to-be-filtered three-dimensional point cloud, and obtaining a normal vector of each point; for each query point in the to-be-filtered three-dimensional point cloud, obtaining a plurality of nearest neighbor points as neighbor points based on geometric distances; calculating a joint weighted distance between each query point and each neighbor point according to the normal vectors of each query point and each neighbor point; determining a local statistical index of each query point based on the joint weighted distance between each query point and all neighbor points thereof; determining a global threshold value based on the local statistical indexes of all query points; and judging the query points of which the local statistical indexes are greater than the global threshold as outliers, and removing the outliers to realize three-dimensional point cloud denoising. By adopting the method, the denoising precision of the three-dimensional point cloud can be improved, and meanwhile, the integrity of a real geometric structure is guaranteed.

The application discloses a kind of sensing fabric surface texture identification methods based on intelligent sensor, it is related to texture identification technical field, comprising the following steps: S1, output triboelectric signal sequence;S2, output texture friction high-frequency signal sequence;S3, construct standardization microtexture feature vector;S4, utilize improved DeepWalk model to combine anisotropic diffusion kernel simulation manifold diffusion process, by tensor ring decomposition and tangent space alignment aggregation mapping is low-dimensional embedding, based on tangent space relative geometric distance executes classification decision, output initial texture identification result;S5, output logic optimal texture category;S6, output final texture identification result;S7, the consistency of identification result and the surface to be measured is verified by tactile interaction.The application overcomes the limitation that prior art ignores contact mechanism, lacks essential description and lacks verification means, significantly improves the robustness and accuracy of texture identification.

The invention discloses a graph rendering method and device, a storage medium, equipment and a program product, and the method comprises the steps: determining a target surface patch used for generating a liquid drop effect, and generating a plurality of virtual particles in a three-dimensional space corresponding to the target surface patch; constructing a plurality of geometric distance fields corresponding to the plurality of virtual particles based on the spatial position information and the volume parameters of the plurality of virtual particles; performing smooth fusion processing on the plurality of geometric distance fields to obtain a composite distance field representing the droplet form; and performing droplet effect rendering on the target patch based on the composite distance field to obtain a display picture with a droplet effect. According to the scheme provided by the embodiment of the invention, the technical effect of remarkably enhancing the spatial reality sense and the dynamic visual effect of the liquid drops can be achieved.

The application provides a space intelligent path generation method and device based on a space function topology and a medium, belongs to the technical field of space intelligence, and can make space intelligent navigation and path planning technology not only depend on geometric distance, but also model space function semantics and topological logical constraints. The space intelligent path generation method based on the space function topology comprises the following steps: acquiring structural information of a space environment to construct a topological graph, wherein the structural information comprises space structural information and function area information; performing information propagation and aggregation on nodes of the topological graph by using a graph neural network to generate a global topological embedding matrix; and selecting an optimal path between nodes based on the global topological embedding matrix to make the path reachable in a physical space and meet logical constraints. The application exemplarily discloses a space intelligent path generation device based on a space function topology and a computer storage medium.

The invention belongs to the technical field of earthquake magnitude judgment, and particularly discloses an HR-GNSS major earthquake magnitude prediction method fusing physical perception and an attention mechanism. The method comprises the following steps: firstly, acquiring HR-GNSS three-component waveform time sequence data of a plurality of stations, extracting time sequence features through multi-layer convolution, and performing compression along a time dimension to form a feature matrix corresponding to the plurality of stations; then, a dot product self-attention mechanism is introduced into the station dimension, correlation among different station features is constructed, information interaction is carried out, and a global waveform feature vector used for representing a seismic event is output; the magnitude prediction model also takes the epicentral distance of each station as an auxiliary input, and extracts a geometric distance feature vector of station spatial distribution from an epicentral distance vector through a multilayer perceptron. And finally, fusing the obtained global waveform feature vector and the geometric distance feature vector in a high-dimensional space, and outputting a moment magnitude prediction result through a full-connection regression module. The method can quickly and accurately estimate the magnitude of the strong earthquake.

The invention relates to the technical field of knowledge maps, in particular to a knowledge map embedding method based on quaternion double bidirectional rotation and dynamic translation and application of the knowledge map embedding method. Mapping entities and relationships in the knowledge graph into quaternion vectors, and performing semantic transformation on a head entity and a tail entity by using a double bidirectional rotation mechanism to obtain a semantic matching score; performing geometric transformation on the head entity and the tail entity by using a dynamic rotation translation mechanism to obtain a geometric distance score; and finally, fusing the semantic matching score and the geometric distance score to obtain a final score of the triple. And updating model parameters according to the final score, and performing link prediction through the updated model. The method aims to solve the problem of inaccurate query results caused by incomplete knowledge maps and limited expression ability of existing models in intelligent search systems and the like, so that the performance of an application level is remarkably improved.

This invention discloses a 3D visual language model pruning method based on perceptual semantic distance and spatial geometric distance, relating to the fields of multimodal artificial intelligence and 3D scene understanding. The method includes: calculating the importance score of each token based on the visual encoder's self-attention matrix, sorting the tokens by score, and selecting the top n tokens as salient tokens; backprojecting the remaining tokens, along with the depth map and camera intrinsic and extrinsic parameters, onto a unified world coordinate system to construct a fusion metric that simultaneously considers semantic similarity and 3D geometric distance, and selecting m diverse tokens using a farthest point incremental sampling method; finally, concatenating the salient tokens and diverse tokens and inputting them into a language model to complete 3D question-answering reasoning. This invention can be applied to existing multi-view 3D question-answering systems without additional training, significantly reducing the number of visual tokens and reasoning latency while maintaining 3D reasoning capabilities.

An attribute encoding method, an attribute decoding method, and an electronic device. The attribute encoding method includes: determining, by an encoding end, K first target neighboring points in a plurality of candidate neighboring points based on a geometric distance and an attribute distance between a to-be-encoded point and each candidate neighboring point in the plurality of candidate neighboring points; performing, by the encoding end, attribute prediction on the to-be-encoded point based on an attribute value of each first target neighboring point to obtain an attribute predicted value of the to-be-encoded point; and encoding, by the encoding end, an attribute residual of the to-be-encoded point to generate an encoded bitstream corresponding to the to-be-encoded point; where the attribute residual of the to-be-encoded point is determined based on the attribute predicted value of the to-be-encoded point and an original attribute value of the to-be-encoded point.

The invention provides a flywheel panel semi-finished product flatness detection method based on laser transmission and diffraction images. The flywheel panel semi-finished product flatness detection method comprises the steps that fringes generated by laser transmission DOE diffraction gratings are projected to the surface of a flywheel panel semi-finished product, and then images are collected; distortion correction is carried out under camera geometric calibration constraint, and a transformation relation between a camera coordinate system and a world coordinate system is established; phase information of a high-quality fringe image is extracted through fast Fourier transform, or phase reconstruction is carried out on an image with poor fringe quality through a neural network; converting the phase information into three-dimensional point cloud data under the constraint of gauge block height calibration; and for the three-dimensional point cloud data, screening out inner points by using a random sampling consistency algorithm, fitting out a reference plane based on least square, and detecting the flatness of the flywheel disc semi-finished product by calculating the geometric distance from each inner point to the reference plane. According to the method, the defects of manual detection and a traditional laser line scanning mode in the aspects of efficiency and reflection resistance are overcome, and rapid and accurate automatic detection is achieved.

The invention relates to the technical field of satellite navigation positioning, in particular to a GNSS differential phase deviation estimation positioning method, system and device and a medium, geometric distance parameter interference is eliminated and differential phase deviation is separated through a multi-frequency non-geometric phase combination equation, and a dynamic parameterized model is established to realize continuous tracking of time-varying deviation; a deviation physical characteristic model is constructed based on the time sequence resolving data, and a dynamic compensation mechanism of multi-frequency phase observation is formed; parameters such as receiver position, clock error and ionosphere delay are solved in combination with a non-combined positioning frame, and the precision and convergence efficiency of precise single-point positioning are improved; the method breaks through the limitation of traditional static deviation, supports multi-system multi-frequency observation fusion processing, solves the problem of hardware delay error correction in non-integrated positioning, and provides a systematic solution for high-precision navigation positioning in a complex scene.

The invention discloses a GNSS ambiguity fixed checking method and device and a storage medium. The objective of the invention is to improve the reliability of an ambiguity fixing result in high-precision positioning of a global navigation satellite system (GNSS). The method comprises the following steps: acquiring a dual-frequency carrier phase observation value of a GNSS receiver; determining an integer solution of the dual-frequency integer ambiguity through parameter estimation and integer search based on the observation value; substituting the integer solution into a dual-frequency carrier phase observation equation, and extracting pure observation value noise as a check quantity by constructing a geometric distance elimination combination; and judging whether the double-frequency integer ambiguity integer solution is reliable or not according to a comparison result of the absolute value of the observation value noise and a preset reliability threshold. According to the method, through direct verification of physical observation quantity, the technical problem of ambiguity error fixation in a complex observation environment is effectively solved, and the credibility of a high-precision positioning result is remarkably improved on the premise that the positioning precision and the model strength are not lost.