132 results about "Minimum distance" patented technology

The invention discloses a ship complex HRRP estimation method and a ship complex HRRP estimation system matched with non-Gaussian characteristics of sea clutters. The method comprises the following steps: acquiring original radar echo data; a threshold detection method is adopted for the original radar echo data to obtain a distance interval where the ship multiple HRRP is located; representing the radar echo signals in the distance interval as a vector matrix model; adopting a K-S minimum distance criterion to select a composite Gaussian model matched with the statistical characteristics of the interference vector in the vector matrix model; and estimating the ship complex HRRP by adopting a sparse optimization method corresponding to the composite Gaussian model. According to the method, a single-parameter probability model is adopted to constrain the sparsity of the ship multiple HRRP, a K-S minimum distance criterion is adopted to determine which composite Gaussian model is adopted to constrain the statistical characteristics of sea clutters, and A-D test is adopted to determine the sparse parameter q of the ship multiple HRRP; and the performance loss of ship multi-HRRP estimation caused by clutter model mismatch is obviously reduced.

The invention discloses a rigid body obstacle avoidance method based on three-dimensional discrete model distance calculation, and the method comprises the steps: building a BVH for a rigid body and an obstacle model in a preprocessing stage, guaranteeing the BVH to be a full binary tree, and initializing two buffer regions to record a BVTT front line and the minimum distance upper bound of two bounding boxes; the method comprises the following steps: iteratively expanding BVTT nodes in a buffer area until all BVH nodes in the BVTT are leaf nodes, and calculating the minimum distance of primitives between the leaf nodes; in each iteration, a self-adaptive extension depth is calculated according to the size of the current buffer area, then all BVTT nodes in the existing buffer area are extended and stored in another buffer area, and then two BVTT buffer areas are switched by using a double-buffer area mechanism; and judging whether the rigid body performs obstacle avoidance according to the minimum distance. According to the method, the bounding box elimination efficiency is improved, minimum distance calculation is applied to a rigid body simulation scene, and efficient real-time obstacle avoidance is realized.

The invention discloses a ship intelligent fault diagnosis method and system based on open label space identification. The method comprises the following steps: obtaining a multi-source sensor time sequence signal of a ship system and constructing a training sample set; carrying out feature extraction on the training sample set by utilizing a deep neural network model, and strengthening the clustering characteristics of the features by adopting a center loss function in the training process; training a classifier on the basis of feature extraction and introducing an open set loss function to form a comprehensive objective function; extracting features from a to-be-diagnosed sample, embedding the features, calculating the distance between the to-be-diagnosed sample and a known fault category feature center, and judging an unknown fault through comparison between the minimum distance and a preset threshold value; and outputting a known fault category or triggering an unknown fault alarm according to a judgment result, and dynamically expanding and updating a model knowledge base based on accumulated unknown fault samples. The method can break through the limitation of the traditional closed set hypothesis, effectively identifies the unknown fault type, and achieves the self-adaptive learning and continuous optimization of a ship fault diagnosis system.

The invention discloses a dual-machine target positioning method based on semantic prior and spatial intersection constraint. The method comprises the following steps: 1, acquiring images of a camera 1 and a camera 2, and determining the spatial orientation of a camera coordinate system relative to a reference world coordinate system; step 2, obtaining a target normalized coordinate under a coordinate system of the camera 1; step 3, obtaining a target normalized coordinate under a coordinate system of the camera 2; 4, converting the normalized coordinates of the target into geometric rays in a three-dimensional space; 5, constructing an optimization function with the minimum distance sum of squares; step 6, performing semantic-level foreground and background division on the image content through a YOLOv5 network, and constructing a three-dimensional space feature point library of a semantic background; and 7, performing single-view semantic neighborhood retrieval and depth compensation by using the three-dimensional space feature point library of the semantic background constructed in the step 6, and realizing target three-dimensional positioning in a high-altitude long-distance complex scene. According to the invention, the detection robustness and the positioning precision of the high-altitude long-distance target in the environment are improved.

The invention relates to the technical field of building information, in particular to an LOD generation method of a BIM model, which comprises the following steps: S10, acquiring information parameters of all edges in a model grid, and acquiring the data volume and secondary error of each edge according to the information parameters, S20, deleting the edge length with the minimum secondary error, and S30, deleting the edge length with the minimum secondary error. S30, simulating a length value of a triangular mesh mapped to a screen according to a screen space error, and obtaining a pixel size of a longest side mapped to the screen and a mesh error according to the length value, and S40, when the model precision is switched, obtaining a new point with the minimum distance to all planes of the deleted side end points, and updating the new point to the model mesh. And if the grid error meets a preset value, generating the LOD level as a rendering target. By the adoption of the method, it can be guaranteed that the LOD contains rich information and complete building element representation, the LOD of the proper level is generated, manual intervention is reduced, and the generation efficiency is improved.

The embodiment of the invention discloses a three-dimensional tennis track real-time reconstruction method and system based on multiple cameras, internal and external parameters and calibration results of the multiple cameras are utilized, respective two-dimensional pixel coordinates are converted into three-dimensional rays, and nodes are positioned in combination with a minimum distance algorithm. According to the embodiment of the invention, the real-time reconstruction of the three-dimensional track of the tennis ball is realized by virtue of the synchronous multi-camera two-dimensional image through the steps of camera accurate calibration, two-dimensional to three-dimensional ray conversion, intersection point calculation and screening, data output and the like. Compared with the prior art, the reconstruction precision is improved through accurate calibration and ray intersection screening; by means of multi-camera data synchronization and simple calculation process, data delay is reduced, and real-time performance is prominent; by means of multi-camera data redundancy and a placeholder mechanism, the robustness in a complex environment is enhanced; the output three-dimensional point data can directly support the functions of ball speed calculation and the like, and the expansibility is good.

The invention provides an unknown malicious traffic detection method and device. The method comprises the following steps: converting a network stream to be detected into a grey-scale map; an encoder in the trained generative model is utilized to extract encoding features of the grey-scale map, the minimum distance between the encoding features and Gaussian prototypes corresponding to all known traffic categories is calculated, if the minimum distance is smaller than a preset threshold value, the network flow to be detected is considered to belong to the traffic category corresponding to the minimum distance, and otherwise, the network flow to be detected is considered to be an unknown traffic category; wherein the training process of the generative model comprises the following steps of: constructing a traffic data set marked with category labels; converting each network flow sample in the flow data set into a grey-scale map, sequentially inputting all the grey-scale maps into a conditional variation auto-encoder, calculating ELBO loss according to a generation limit, calculating identification loss according to an identification limit, and calculating total loss to update network parameters; after training is completed, the conditional variation auto-encoder is a generative model, and Gaussian prototypes corresponding to all known traffic categories are obtained.

The invention relates to a deep learning signal open-set identification method based on dynamic matching comparison, and the method comprises the steps: processing all signals to obtain a training sample, constructing a data set, training batch data, and transmitting the data to a deep learning network for forward propagation, thereby obtaining 2k high-order semantic features; euclidean distance calculation and activation function nonlinear processing are carried out on the first k high-order semantic features and the last k high-order semantic features, and signals corresponding to the minimum distance are found and sorted; back propagation is carried out on the deep learning network, all weight parameters are updated, the data set training batch number is constructed again, and after training of the deep learning model is stopped, features are extracted to construct a feature matching sample library; and performing feature extraction after processing the to-be-matched signal data, performing Euclidean distance calculation on the extracted features and all features in the feature matching sample library, and performing identification and judgment according to a calculation result. According to the invention, a known signal target can be identified, and an unknown signal target can also be identified.

The invention discloses a two-stage indoor positioning method and system based on MWKNN-PF, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining a positioning region, constructing an equidistant grid, and obtaining the receiving signal data of fingerprint points, and each grid intersection point is a fingerprint point; obtaining a vector and a covariance of each fingerprint point according to the received signal data, and constructing a fingerprint database; obtaining a plurality of clusters according to a clustering algorithm; receiving signal data is extracted, and a cluster with the minimum distance is obtained; obtaining the number of adjacent points according to the dynamic selection, and obtaining an initial positioning coordinate according to the weight of the plurality of adjacent points; constructing a prediction model according to the state transition model; the initial positioning result based on the MWKNN algorithm serves as an actual measurement value, particle filtering is conducted on the target state, the accurate position of the target at the # imgabs0 # moment is obtained, compared with the WKNN algorithm, the calculation load of fingerprint matching work in the online stage is greatly reduced, and the overall real-time performance is improved by reducing the calculation load.

The embodiment of the invention discloses a moving body track planning method and device, electronic equipment and a storage medium, and relates to the technical field of track planning, and the method comprises the steps: carrying out the geometric boundary expansion and minimum distance detection of a moving body, and obtaining the environment complexity of a starting point and an end point of the moving body, the method comprises the steps of obtaining an initial trajectory and constructing kinematic constraints based on a mixed A * algorithm of a bidirectional search strategy, constructing a safety corridor based on the initial trajectory to obtain obstacle avoidance constraints, calculating environment complexity of each trajectory point according to the safety corridor, classifying the trajectory points to obtain sampling time constraints, and obtaining final constraints according to the constraints. Using the initial trajectory as an initial solution to obtain a planned trajectory, performing transverse and longitudinal decoupling on the planned trajectory to respectively obtain transverse and longitudinal state equations, and performing tracking control on the moving body by adopting a model predictive control (MPC) algorithm. According to the method, the problems of low calculation efficiency, infeasible trajectory planning result and poor optimization process convergence in the prior art are solved.

The invention discloses a tobacco leaf thickness measurement method, device and equipment and a storage medium, and relates to the technical field of thickness measurement, and the method comprises the steps: determining an original data point based on each laser stripe center point on a laser stripe formed when a line laser is projected to a target to-be-measured tobacco leaf, determining a to-be-selected straight line slope sample data set based on coordinate values of any two data points in the original data points on a preset coordinate axis; determining initial values of slope sample centroids corresponding to all slope candidate data in the candidate straight slope sample data set, and determining a target straight slope based on the initial values and a preset straight slope determination algorithm; the preset straight slope determination algorithm is a mean shift algorithm determined based on a Gaussian kernel function; and determining a fitting straight line by using the target straight line slope and a preset minimum distance quadratic sum rule so as to determine the thickness of the target to-be-measured tobacco leaf based on the fitting straight line. The straight line fitting method constructed based on the preset straight line slope determination algorithm can improve the accuracy of measuring the tobacco leaves to be measured.

A method includes receiving a data population and a plurality of constraints. The data population includes a plurality of units. Each unit includes a plurality of respective values. Each value corresponds to a respective parameter. Each constraint corresponds to a respective parameter. A holistic feature of the data population is determined. A volume is defined in the parameter space based on the plurality of constraints. For each unit of a current data population, a first vector representing a respective unit is subtracted from a second vector representing a current holistic feature to determine a third vector. For each third vector, a distance between a point defined by a respective third vector and the volume is determined. A minimum distance is determined from determined distances. The current data population is transformed into an updated data population by removing a unit that corresponds to the minimum distance from the current data population.

The invention relates to a contact analysis method suitable for a complex curved surface of any key component, and the method comprises the following steps: S1, obtaining m * n three-dimensional point set data of the curved surface through a measuring instrument, and constructing a digital curved surface mesh model; s2, proposing a rough contact state solving method taking the minimum distance between the cross point sets of the two curved surfaces as an iteration target; s3, establishing a three-dimensional grid point set interpolation method based on linear interpolation; s4, performing fine contact state solving based on the finer curved surface mesh and the relative position of the two curved surfaces at the coarse contact moment; the problems that an existing complex curved surface expression is difficult to obtain, the association rule of design parameters and the contact state is not clear, and an existing design method cannot accurately analyze the contact state of the complex curved surface, cannot accurately describe the contact state of the complex curved surface and cannot well help optimization design and manufacturing of key parts are solved.

The invention relates to the technical field of computer aided design, in particular to a neighborhood gradient-oriented large-curvature curved surface projection point calculation method, which comprises the following steps of: S1, calculating a space bounding box of a curved surface to be projected, and performing triangulation to generate a triangular mesh; s2, traversing curved surface control vertexes of the triangular mesh, recording an adjacent vertex set, and judging whether the vertex set is a boundary point or not according to the UV parameter coordinates; s3, storing the vertex information and constructing a BVH tree, traversing the BVH tree for a to-be-projected point, querying and obtaining the nearest vertex with the minimum distance from the to-be-projected point, judging whether the nearest vertex is a boundary point or not, and processing according to a judgment result to obtain an iteration initial value; and S4, constructing a target function by taking the iteration initial value as an initial value, and calculating by using a Newton iteration method to obtain an accurate projection point of the to-be-projected point on the curved surface. The curved surface projection point approximation result calculation method is high in calculation efficiency and robustness, and the influence of curvature, grid division and other factors on Newton iteration input is reduced.

The invention discloses a three-dimensional CAD model collision detection method based on BVH and quadratic sum planning. The method comprises the steps that firstly, BVH is constructed for two geometric objects; secondly, the minimum distance and the maximum distance between AABB bounding boxes of BVH leaf nodes of the two nodes are calculated, the minimum maximum distance is recorded, and all node pairs with the minimum distance smaller than or equal to the minimum maximum distance are stored; then, according to information in each node pair, a quadratic sum plan is set to solve an objective function of a minimum distance, a variable and a constraint, the quadratic sum plan is converted into semi-definite planning for solving by utilizing an optimization solver, and the minimum distance of one node pair is obtained, so that the minimum distance between the two geometric objects is obtained; and finally, comparing the minimum distance with a threshold value in an actual collision detection scene, and judging whether the two geometric objects collide or not. According to the method, a unified algorithm process is designed for solving the minimum distance between various types of geometric objects.

The invention discloses a motion multi-target matching and position estimation method based on an asynchronous image sequence, and belongs to the fields of computer vision, SLAM, photogrammetry and the like. Firstly, three cameras are adopted to shoot sequence images, three space planes are established for each target, candidate tracks of the targets are determined for every two planes, candidate positions at specific heights are calculated, and then a candidate position matrix of multiple targets between the camera 1 and the camera 2 and a candidate position matrix of multiple targets between the camera 1 and the camera 3 are obtained; secondly, establishing a flexible search method according to a principle that the distance is minimum as a correct matching principle, performing undifferentiated distance calculation on the two matrixes to obtain multi-target matching under the condition of the minimum distance, and establishing a position estimation algorithm based on line-plane constraint according to a matching result; according to the method, the problem that the existing algorithm cannot perform multi-target matching and position estimation under the condition of asynchronism and no obvious characteristics is solved, no relation exists between cameras in the calculation process, the adaptability is higher, and the equipment structure is simpler.

The invention discloses a reservoir damming branch blocking information extraction method and device based on a digital elevation model, and the method comprises the steps: obtaining a reservoir digital elevation model and key parameters, and extracting a corresponding water level isoline data set, a range boundary and slope data; after contour line data sets are classified, different types of pattern spot data sets are formed through surface construction and space relation operation; performing spatial inclusion relation query and judgment processing on the pattern spot data set to form a damming water area pattern spot object set; the object set is traversed, the nearest point and the minimum distance between the object set and the main flood prevention limited water level pattern spots or the object set are calculated, a damming and blocking branch object is formed, and the dam crest plane area, the average elevation, the loss reservoir capacity of the damming and blocking branch and the influence reservoir capacity are calculated. According to the method, the technical problems of limited precision, incomplete information extraction and the like due to the fact that the current damming and blocking branch information depends on manual identification are solved, and a basis is provided for reservoir flood control capacity analysis and damming and blocking branch rectification decision making.

The invention relates to the technical field of target scheduling and warehouse logistics, in particular to a multi-vehicle scheduling method based on a genetic algorithm. Comprising the steps of generating an initial solution by adopting an improved repair mode; taking the total time as a target, performing roulette selection on parents according to the fitness, and performing crossover and mutation operation to generate offspring; repeating improvement and restoration in the initial solution for the filial generation; combining the parent generation and the child generation, and reserving individuals with high fitness to enter the next generation; with the conflict probability as a target, multi-vehicle trajectory planning is optimized through timestamp estimation and an artificial potential field; taking the conflict cost and the total distance as optimization targets, and performing target optimization through a non-dominated sorting genetic algorithm to obtain a non-dominated solution; time and conflict cost are normalized, the Euclidean distance between each solution and an ideal point is calculated, and the solution with the minimum distance is selected as the optimal solution. The method has the advantages that multi-objective optimization is realized through non-dominated sorting and crowding degree distance calculation; conflict risks are considered while global planning is performed, and a reasonable route is planned.

The invention belongs to the technical field of finite element model generation and numerical simulation, and particularly relates to a foam structure model design method based on gradient Voronoi. The method specifically comprises the steps that firstly, N control points are continuously and randomly generated in a three-dimensional space area V0, the generated control points meet the condition that the Euclidean distance dist (Qi, Qj) between every two adjacent control points is larger than the minimum distance Dmin, and the minimum distance Dmin is determined based on a gradient function gamma (X); 2, sequentially calculating vertical bisecting surfaces of a connecting line between two adjacent control points, respectively offsetting the generated vertical bisecting surfaces by a set distance along the positive and negative directions of the connecting line of the two control points, and forming a Voronoi polyhedron with actual wall thickness by two new vertical bisecting surfaces generated by offsetting; and step 3, generating a gradient foam structure numerical model based on the Voronoi polyhedron with the actual wall thickness.

The invention discloses a high-specific-speed rotating blade multi-source constraint design method based on an improved grey wolf algorithm and Poisson disc sampling, and belongs to the technical field of high-specific-speed rotating blade design. The method comprises the following steps: firstly, constructing a multi-source constraint region, giving an initial model and characteristic simulation data thereof, and setting meridians and other geometric characteristics of the front edge and rear edge contours of a blade; calculating the inlet angle of the initial model blade and the vortex velocity difference of the front and rear edges of the blade, screening design parameters, and calculating and determining the design variable range through the dimensionless vortex velocity. Then, uniformly sampling in the limited area by using a Poisson disk sampling algorithm, ensuring that sampling points are not mutually overlapped, and dynamically updating a sampling set; and finally, applying a multi-objective optimization equation, constructing an agent model, obtaining a Pareto solution set through an improved grey wolf optimization algorithm, and selecting an optimal parameter combination from the Pareto solution set to improve the design precision and performance of the rotating blade. The method has the characteristics of no deviation, minimum distance and maximum sampling area coverage.

The invention belongs to the technical field of machine learning and mode recognition, and particularly relates to an intuitionistic fuzzy double support vector machine classification method based on relative density and non-membership, which comprises the following steps: constructing and training an RD-IFTSVM model, and performing data classification through the trained RD-IFTSVM model. According to the method, a relative density membership function is provided, the proportion of local neighborhood heterogeneous samples to global similar density consistent samples is combined, misjudgment caused by the fact that a traditional method only depends on local neighborhoods is avoided, noise is restrained, and effective samples are reserved; optimization problems are respectively constructed for positive and negative classes, independent regularization parameters and slack variables are introduced, key protection of core samples is realized through membership weighting, and robustness of noise samples is improved; meanwhile, a double-hyperplane discrimination criterion based on a geometric distance is provided, and the sample attribution is determined by the minimum distance to the positive and negative kernel hyperplanes.

The present invention provides a crustal spectral unit optimization modeling method suitable for simulating strong earthquake wave fields in complex terrain conditions, which relates to the field of earthquake engineering. The method comprises determining the terrain parameters of the study area; determining the grid refinement factor based on the wave velocity structure parameters of the study area, and obtaining the minimum distance of the wave velocity isosurface from the bottom; determining the location of the crustal spectral unit refinement transition layer; determining the total number of three-dimensional units before grid refinement based on the location of the crustal spectral unit refinement transition layer; using hexahedral units to mesh the entire area; refining the shallow crust model based on the number of crustal spectral unit layers above the transition layer; calculating the grid quality, counting the number of crustal spectral units to obtain the expected value of the crustal spectral units, and comparing it with the estimated value. The present invention can solve the quality problem of spectral units when modeling crustal structure in areas with complex terrain conditions such as high mountains and canyons, while minimizing the computational degrees of freedom, and achieving stable and low-cost simulation of earthquake motion based on the spectral element method.

The present invention relates to an efficient adaptive method based on the Kriging model. This method is mainly implemented through three steps: First, a certain number of samples are generated by the Latin hypercube method to construct an initial Kriging model; Second, a certain number of candidate samples are screened from the candidate samples through the current Kriging model, and two samples are determined; Third, the candidate samples and the experimental design samples are updated and the Kriging model is reconstructed, and it is judged whether the requirements are met. If not, jump to the second step. If met, the current Kriging model is used to replace the original model for reliability analysis. The present invention continuously increases the number of sample points near the limit state through adaptive iteration, and determines the added samples by maximizing the minimum distance, so that the iteratively updated samples are approximately evenly distributed near the limit state, the sample points are more fully utilized, and the result of the reliability analysis is more accurate.

The invention discloses a wheel disc over-rotation fracture sequence sampling method based on space reduction and electronic equipment, and belongs to the technical field of experimental design and digital twinning. The method comprises the following steps: generating an initial sample set and constructing an initial agent model; dividing concerned and unconcerned areas based on the predicted strain amplitude; constructing a comprehensive sampling criterion for fusing the global precision and the strain amplitude of the model; calculating a rejection domain radius which is adaptively reduced along with iteration; solving an optimization problem with maximization as a target and minimum distance > = as a constraint, and determining newly added sample points; and updating the model and carrying out iteration until a precision convergence condition is met. According to the method, through physical mechanism driving and dynamic space regulation and control, automatic focusing sampling is carried out on a high-strain key area, an agent model with higher local precision can be constructed at lower simulation cost, and the efficiency and accuracy of wheel disc fracture rotation speed prediction are remarkably improved.

The invention relates to a method for determining, from a set of point groups, a target set of point groups in an environment (150) which are visible or invisible from a predefined observation point (214) in the environment (150), the point groups comprising one or more points (P, A, B), comprising: providing the set of point groups in the environment having coordinates in an original coordinate system (200), transforming coordinates of the set of point groups into spherical coordinates in a target coordinate system, wherein an observation point (214) in the environment is located at an origin of the target coordinate system; determining a spherically curved grid in spherical coordinates in the target coordinate system, wherein the grid comprises grid points and / or cells; assigning each point group of at least a portion of the set to a cell or a grid point, for each of at least a portion of the cells of the grid or for each of at least a portion of the grid points of the grid assigned with more than one point group: determining a minimum spacing and / or a maximum spacing, a) a target set of point groups visible from a predefined observation point (214) in the environment is determined, and / or b) a target set of point groups invisible from a predefined observation point in the environment is determined, and the target set or the target set of point groups is provided.

The embodiment of the invention provides a method for determining a distance parameter, a device for determining the distance parameter, electronic equipment and a non-volatile computer readable storage medium. The method comprises the steps that a first set and a second set are obtained, the first set comprises at least one first geometric object, the second set comprises at least one second geometric object, and the first geometric object and the second geometric object are compact and convex; iteratively updating the first set or the second set; and determining a distance parameter of the first set relative to the second set based on the updated first set and the updated second set. According to the embodiment of the invention, the minimum distance and the nearest point pair between two sets comprising a plurality of geometric objects can be efficiently calculated, so that collision detection and distance parameter determination can be efficiently carried out, and the requirements of collision detection and distance testing in a complex scene are met.

The invention discloses a space target collision early warning method based on GPU parallel computing and a J2 perturbation item, aims to solve the problems of high computing complexity and poor real-time performance of existing CPU processing space target collision risk assessment, and provides a multi-stage screening method based on GPU parallel computing and the J2 perturbation item. The method comprises the following steps: reading a TLE and calculating near / distant points and a reference check point at a GPU (Graphics Processing Unit); combining according to C (N, 2) and screening according to epoch, height and geocentric distance; taking a check point state as an initial value in a check interval, and adopting single-precision J2-RK iterative screening to obtain events NJ2pre and tJ2pre; the events are screened by using double-precision J2-RK in front and back check sections, and minimum distances distJ2 and tJ2 are output; and finally, SGP4 / SDP4 is used for parallel actuarial calculation to obtain a minimum distance distDC, a relative speed, a collision probability and the like. The method gives consideration to real-time performance and precision, significantly reduces the O (n) calculation cost, and is suitable for large-scale in-orbit target early warning.

The invention discloses a modeling method for solving the distance from a sample point to a simplex topological space in a high-dimensional space. The modeling method is used for efficiently calculating the minimum distance from the sample point to a high-dimensional simplex. By means of the modeling method, the calculation complexity can be effectively reduced, the calculation efficiency and stability are improved, and therefore a more advanced, efficient and reliable solution is provided for solving the distance from the midpoint to the simplex in the high-dimensional space. The method aims at overcoming the defects in the prior art, can quickly and accurately calculate the distance from a sample point to simplex in a high-dimensional space, and has important significance for promoting research and technical development in related fields.

A sealed compression measurement method, a storage medium and an equipment relate to the technical field of automation detection, and solve the problems of low efficiency and poor measurement accuracy of the existing manual measurement method. The method comprises the following steps: S1, fitting models of a measured sealing element and a measurement reference element in a free state according to actual assembly positions of the measured sealing element and the measurement reference element, and cutting two-dimensional sections of the measured sealing element and the measurement reference element; S2, marking the measured sealing element and the measurement reference element; S3, capturing an intersection of the measured sealing element and the measurement reference element; S4, establishing a measurement reference surface of the measurement reference element; S5, capturing a farthest point i of the measured sealing element on a side compressed to the reference surface; and S6, measuring a minimum distance of the point i to the measurement reference surface, that is, a compression amount.

The invention discloses a rendering method of a fragmentation model in a virtual scene and a related device. The method comprises the following steps: for each fragment model included in a fragmentation model in a virtual scene, determining a target frame center point position of the fragment model through a plurality of target frame vertex positions of the fragment model, and determining the maximum distance from the vertex to the center point of the fragment model, namely the maximum collision size; obtaining a minimum distance, namely a distance field value, from the center point of a scene voxel to which the center point position of the target frame in the virtual scene belongs to the scene surface; under the condition of determining that the fragment model collides with the scene surface based on the maximum collision size and the distance field value, adjusting the center point position of a target frame through a direction vector of the distance field value, the moving speed of the target frame of the fragment model and the interval time of adjacent frames to obtain an adjusted position; and rendering the fragment model based on the adjusted position to obtain a rendering result of the fragment model. According to the method, the penetration phenomenon between the fragment model and the scene surface in the rendering result can be avoided, and the rendering effect is improved.