100 results about "Computational geometry" patented technology

The invention relates to the technical field of size detection, in particular to a component size detection system based on machine vision, which comprises an interference area identification module, an edge repair module, a structure clustering module, a distortion analysis module and an attitude restoration module. According to the method, the gray level change trend of each pixel and the adjacent points in the image is obtained, the high-reflection suspected interference area is identified, the edge response cancellation processing is executed, and linear interpolation compensation is carried out by combining the effective edge response values of the adjacent areas; after a continuous contour trend is constructed, clustering and sub-grouping division are carried out according to an included angle difference trend between contour geometric vectors, a three-axis direction response linear array variation amplitude is extracted to construct a distortion curvature mapping table, existence of non-equal-interval deformation is judged, reverse angle compensation is applied, and a three-axis intersection point convergence process is completed through a nonlinear optimization algorithm; and according to the three-dimensional coordinates, calculating a geometric dimension and outputting a detection result, thereby realizing self-adaptive detection of different postures and surface interference component dimensions.

The invention discloses a polygonal and quadrilateral mesh automatic subdivision method based on deep reinforcement learning, relates to the field of computational geometry and mesh generation, and constructs an automatic subdivision framework fusing geometric priori knowledge and a data driving strategy. According to the method, nine basic topology filling templates covering triangles to hexagons are predefined as discrete action spaces, and continuous geometric segmentation is converted into a sequence decision problem; learning a mapping relation between the polygon state characteristics and the optimal template selection strategy by using a deep Q network, and balancing exploration and utilization through a dynamic epsilon-greedy mechanism; and in combination with a vertex number priority scheduling strategy and a multi-dimensional quality award function, guiding an intelligent agent to adaptively generate a high-quality quadrilateral grid. According to the method, full-automatic and high-robustness subdivision of the complex polygon area is achieved, the grid orthogonality and the length-width ratio quality are remarkably improved, manual intervention is avoided, and the method is suitable for engineering scenes such as finite element analysis.

The invention discloses a topological network global optimization method based on graph reinforcement learning, and relates to the field of computational geometry and grid optimizing.The method comprises the steps that a topological optimization process is modeled into a Markov decision process, a topological network containing singular points and angular points is converted into heterogeneous graph representation, and a graph state is constructed; selecting a topology editing action by a reinforcement learning agent fused with the graph neural network based on the current graph state; after executing the action, updating a topological structure and a graph state, and calculating an instant reward based on the global grid quality index change; training intelligent agents in batches through experience playback and time sequence differential learning, so that the intelligent agents learn a mapping strategy from a graph state to an optimal action; and applying the trained policy network deployment to automatic optimization of the new topology. According to the method, full-process automation of topological optimization is achieved, non-local negative effects of local modification are effectively avoided, a global optimal optimization strategy is learned, the grid quality and optimization efficiency are remarkably improved, and the method is suitable for simulation grid generation of aircrafts.

The invention discloses a dynamic obstacle recognition and avoidance method based on laser and visual feature level fusion, and relates to the technical field of robot autonomous obstacle avoidance. According to the method, a laser radar and an industrial camera are arranged on the head or the front of a robot, point cloud and image data are collected in real time, geometric features and semantic features are extracted, and the robot autonomous obstacle avoidance is achieved. Generating a fusion feature set F1 through adaptive geometric semantic mapping and graph structure feature fusion; calculating a geometric semantic consistency index CSI based on the geometric offset, the semantic feature gradient and the reprojection residual error, and performing dynamic correction; a modal reliability weight index MRI is calculated through the CWG-Net, and bimodal features are compensated to generate F2; further obtaining the structural stability, the behavior sudden change probability and the visibility of the dynamic obstacle, calculating a dynamic behavior risk index DRI, marking a dangerous obstacle and planning an obstacle avoidance path; and a dynamic obstacle avoidance strategy is generated by using the prediction model and closed-loop control is performed, so that safe autonomous obstacle avoidance and real-time response of the robot in a complex dynamic environment are realized.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention provides a GIS buffer generation method and system based on fuzzy semantics and geographic constraints, and belongs to the technical field of geographic information systems.The method comprises the steps that fuzzy descriptors, membership values and geographic entity types are input into a pre-trained language-space mapping model, and basic distance parameters are obtained; querying a spatial database based on the geographic entity keywords, obtaining geometric features associated with the geographic entities, and calculating feature lengths corresponding to the geometric features; correcting the basic distance parameter based on the intention direction, the membership value, the geographic entity type and the feature length expressed by the fuzzy description word to obtain a geographic distance threshold; generating an initial two-dimensional buffer polygon based on the geographic distance threshold and the spatial geometric features of the geographic entity; and querying a digital elevation model to obtain topographic data based on the initial two-dimensional buffer region polygon, and generating a three-dimensional buffer region curved surface through a gradient sensitive interpolation algorithm. According to the invention, the accuracy of generating the GIS buffer area is improved.

The invention provides a method for generating a highway engineering feasibility research report based on local large model training, which is based on a locally deployed large model, integrates multi-source data, and realizes mapping and verification from a fuzzy policy to a structured field through regular extraction of a standard field and construction of a highway engineering knowledge graph. The method adopts TensorRT to accelerate reasoning, injects a specification term table, optimizes term consistency in combination with comparative learning, and generates a specification text through LoRA fine tuning and a reward function. After route parameters are input, geometric features are calculated, a disaster database is associated to recommend a detour scheme, and chapter content is generated. And outputting a formal report based on the template, and ensuring data consistency through cross validation. RPA is adopted to capture standard updating, Kafka and Fiss vector libraries are combined to achieve compliance verification, knowledge graph incremental updating and conflict correction are triggered, and a full-process automatic and intelligent feasibility research report generation system is formed.

The invention provides a logistics express sheet automatic identification and express sheet number extraction method based on rule configuration, and relates to the technical field of data processing, and the method comprises the steps: 1, carrying out the layout structure analysis and geometric correction of an input logistics express sheet image, calculating geometric correction parameters through evaluating the deformation degree of the image and combining the structural features of the logistics express sheet, and obtaining a logistics express sheet number; performing correction processing on the logistics express sheet image based on the geometric correction parameter to obtain a standardized image; and 2, performing optical character recognition processing on the standardized image, extracting all character information in the standardized image, performing word segmentation, entity recognition and semantic understanding on the recognized text by utilizing natural language processing, obtaining a key semantic unit, and organizing recognition results into a text data set. According to the invention, automatic identification and bill number extraction of the logistics bill are realized, and the logistics information processing efficiency and accuracy are improved.

The invention provides an adaptive grid generation method and system based on reinforcement learning, and belongs to the field of computer graphics and computational geometry, and the method comprises the steps: S1, according to the input of a known model, constructing a background grid, calculating the curvature characteristics and proximity characteristics of the known model, and generating a corresponding size field; s2, inputting a to-be-predicted unknown model into the information transfer neural network, and predicting to obtain a discrete size field of the unknown model; carrying out interpolation processing on the discrete size field to obtain a continuous size field of the unknown model; and S3, according to the continuous size field and the known model, introducing reinforcement learning, using a leading-edge propulsion method to generate an adaptive grid as a training data set for training the supervised training network, and generating an adaptive grid of an unknown model through the leading-edge propulsion method. On the premise of ensuring the rationality and geometric quality of the grid topological structure, the method remarkably improves the grid precision and overall generalization ability of the key area, and has a wide application prospect.

The invention relates to a visual pose estimation method based on data driving. The method comprises the steps that a visual pose estimation model is constructed, an efficient feature extraction and matching module extracts multi-level depth features of an input image through a lightweight network, and a dense matching point pair set is generated through cross-view matching after local aggregation and down-sampling. The two-stage hierarchical pose estimation module generates a coarse pose through multi-view geometric solution and depth regression result weighted fusion, and calculates a geometric residual error and a confidence score by taking the coarse pose as prior to obtain a pose intermediate result. A micro-prior guided RANSAC optimization module constructs sampling probability distribution, generates a soft weighting coefficient, estimates a geometric model, and outputs a preliminary optimization pose. And constructing a joint loss function, iteratively updating model parameters, and outputting a final camera relative pose by using the trained model. By adopting the method, the estimation precision and the operation efficiency can be remarkably improved while the geometric interpretability is kept.

The invention discloses a graphic interactive numerical control machining method and system. A perceptible digital twin environment integrating geometric, dynamic and thermodynamic multi-source sensor data is constructed; the SLAM and IMU fusion technology is utilized to realize hyperstable alignment of augmented reality and physical space; after graphical processing task definition is carried out in AR, virtual-real fusion rehearsal is carried out through a predictive twin fidelity model, and the stability and risk of the processing process are quantified and predicted by calculating fidelity components of three dimensions of geometry, dynamic and thermodynamics in real time. In rehearsal, the system can find and guide a user to avoid flutter, thermal deformation and other problems in advance. In a physical processing stage, the model continuously drives an adaptive control closed loop, and automatically and smoothly adjusts parameters such as a feed rate according to a predicted fidelity index, or executes safe hovering when a serious risk is predicted.

The invention discloses an interactive catalysis method and platform based on molecular simulation and artificial intelligence. According to the method, intelligent design of a catalyst is achieved through data classification, condition optimization, structure modeling and mechanism analysis. Catalytic data uploaded by a user is subjected to standardization processing and then is divided into two types: a data set A is used for recommending an optimal reaction condition by a multi-objective optimization algorithm; a three-dimensional model is constructed after a catalyst structure of the data set B is optimized through quantum chemistry software, a plurality of intermediates and transition state structures are optimized, and geometric / electronic descriptors are calculated. And constructing a dual-model collaborative prediction system of the model E and the model F through a feature screening and dimension reduction technology, and finally screening the high-performance catalyst by utilizing a model F driven molecule generation algorithm. According to the method, flexible adaptation to specific application scenes can be achieved, the urgent requirements of the industry for efficient and accurate catalyst performance prediction and intelligent design are met, cost reduction and efficiency improvement of the industry are effectively assisted, and the research and development process of the catalyst is accelerated.

The invention discloses a pre-stressed anchor cable monitoring method and system based on a digital force balance anchor cable meter, and belongs to the technical field of engineering structure health monitoring. The system comprises a sensing layer, an edge computing layer and a cloud platform layer, wherein the sensing layer synchronously acquires mechanical (tension, FBG wavelength and the like), vibration, an inclination angle and environmental parameters; the edge calculation layer processes data through two-stage noise reduction; the cloud platform layer constructs a digital twinborn model, and executes early warning and block chain evidence storage. The method comprises the following steps: synchronously acquiring multiple parameters and carrying out space-time alignment; the edge layer outputs effective signals through two-stage noise reduction; performing multi-dimensional anomaly diagnosis, calculating a geometric eccentricity angle and a coupling damage index, and identifying a single-point anomaly and group fault propagation path; and the cloud corrects the force value, predicts the residual life and triggers early warning and compensation. The problems that traditional monitoring information is one-sided, environmental interference is large, response lags behind, and service life prediction precision is low are solved, and intelligent monitoring of the whole life cycle of the anchor cable is achieved.

The invention relates to the technical field of bridge pier underwater detection point distribution, in particular to a deep learning method for a bridge pier underwater detection point distribution scheme, which comprises the following steps: establishing a corresponding calculation geometric region according to the actually measured shape of a bridge pier; determining a point distribution range, and simulating to obtain flow field data in the range; training the flow field data to obtain a compressed code; classifying the compressed codes to obtain a feature set; sampling samples in the feature set to obtain an optimal set with relatively large fluctuation; and selecting the central position of the optimal set to obtain a point distribution scheme of the actually measured piers. According to the pier underwater detection measuring point arrangement method based on time history deep learning, an accurate and universal solution is provided for measuring point position selection of pier underwater detection.

The invention discloses a convex hull algorithm optimization and applicability analysis method based on rotation contraction. The method comprises the following steps: step 1, processing a point set by a rotation contraction preprocessing strategy; and step 2, convex hull calculation. According to the method, the filtering operation is executed, a large number of non-convex hull points in the point set are deleted, the convex hull is calculated based on the classical convex hull algorithm, the convex hull algorithm optimization and applicability analysis method based on rotation contraction is constructed, the computer memory can be saved when the convex hull is calculated based on the method, meanwhile, the convex hull calculation time can be shortened, and the calculation efficiency is improved. And technical support is provided for the fields of computational geometry, computer graphics, computer vision and the like.

The application discloses a safe landing area detection method, device, equipment, medium and product of a UAV, relates to the field of autonomous navigation and safety of a UAV, and comprises the following steps: inputting a visual image into a monocular metric geometry estimation model to obtain metric point cloud data, a surface normal and an effective mask in a camera coordinate system, and transforming the metric point cloud data, the surface normal and the effective mask into a world coordinate system according to pose information; filtering the effective mask to obtain effective point cloud; when the number of the effective point cloud meets a threshold value, performing rasterization processing to obtain a grid unit and calculate geometric features; inputting the visual image into an instance segmentation model to obtain a dangerous area mask, and mapping the dangerous area mask to each grid unit, and combining the geometric features and a ground reference height to determine a safety probability; determining an optimal landing area based on the safety probability, and calculating the center coordinates and the radius of the minimum circumscribed circle of the optimal landing area. The application improves the autonomous safe landing capability of the UAV in an unknown complex environment.

The invention discloses an unmanned aerial vehicle path planning method based on computational geometry and deep reinforcement learning, and the method comprises the steps: constructing a spatial position model of an unmanned aerial vehicle and an object, taking a bandwidth utilization index, a photo coverage rate and an energy consumption index as evaluation indexes, carrying out the training through a reinforcement learning model, and obtaining the optimized shooting motion and path of the unmanned aerial vehicle. The shooting efficiency of the unmanned aerial vehicle and the coverage rate of shooting of the unmanned aerial vehicle on objects can be remarkably improved, the number of redundant photos shot by the unmanned aerial vehicle is remarkably reduced, and therefore bandwidth consumption is reduced.

This invention discloses a ground-air collaborative mapping method based on semantic features and ground plane multi-constraint fusion. The method includes: generating sub-map sets from local point cloud data collected by fire trucks and drones; and generating rotation-invariant semantic descriptors using a maximum height value rasterization strategy and rotation group enhancement. By calculating geometric and semantic distances, and combining isomorphic and heterogeneous pattern weighted fusion, candidate sub-map pairs with loop closures are selected and geometrically registered to generate loop closure constraints. Simultaneously, ground point clouds are extracted using the RANSAC algorithm, and the ground plane model is smoothly updated using a sliding window to construct height and pose alignment constraints. The loop closure constraints, ground plane constraints, and pose information are input into the Ceres optimization framework for joint optimization to generate a globally consistent overall map, effectively improving the accuracy and robustness of ground-air collaborative mapping, and is suitable for emergency scenarios such as complex fire rescue.

The invention discloses a radiation sound field calculation method based on an isogeometric boundary element method, and aims to solve the problems of geometric errors and high calculation cost when a traditional finite element method and a conventional boundary element method are used for calculating a geometric sound field. The method comprises the following steps: firstly, initializing acoustic parameters, defining geometric boundaries based on NURBS, and generating control points, weights and node vectors; then calculating an NURBS primary function, calculating coordinates of configuration points by adopting a Grevile method, and mapping the coordinates to a physical space; then, a boundary integral equation coefficient matrix is assembled, and the integral precision is improved through Gaussian integral and Telles transformation; and solving the surface sound pressure by adopting a GMRES iteration method through boundary conditions, and outputting a response curve or a cloud picture. According to the method, geometric and physical field representation is unified through NURBS, geometric approximation errors are eliminated, CAD / CAE integration is achieved, and the calculation efficiency is remarkably improved. The method supports various boundary conditions, is suitable for non-rigid boundaries and sound-structure coupling scenes, provides sound pressure level visualization results, and assists engineering design optimization.

The invention provides a feature extraction and matching method and system, electronic equipment and a medium, and the method comprises the steps: fusing vehicle point cloud information with a plurality of frames of environment images, and carrying out the preprocessing to generate a point cloud set and a first semantic feature containing a semantic boundary pixel set and a feature region; a geometric error is calculated by projecting a point cloud to an image subjected to semantic segmentation, a semantic region is optimized and boundary broken lines and sawtooth noise are corrected in combination with preset geometric priori so as to enhance geometric constraint and semantic stability of semantic features, and the problem that geometric similar structures are difficult to distinguish is solved. And meanwhile, cross-frame optimization is carried out on multi-frame same-semantic features, single-frame noise is weakened through tracking filtering and time sequence smoothing, time sequence continuous features are generated, and the robustness of a complex scene is improved. The descriptor fusing the semantic boundary and the environment information is generated based on the optimized features, the feature description complexity is compressed, and the distinction degree is improved, so that rapid and accurate inter-frame matching is achieved, and the feature extraction quality and the matching efficiency are effectively improved.

This invention relates to the field of computer technology, providing a method, apparatus, device, storage medium, and program product for process feature recognition. The method includes: inputting a three-dimensional model into a process feature recognition model to obtain recognition results output by the model; for any given process feature, determining the geometric type of each base surface based on the recognition results; determining the parameter calculation method for each base surface based on its geometric type; and calculating the geometric parameters of the process feature based on the parameter calculation method. This invention can automatically calculate geometric parameters, thereby improving the efficiency and reducing the cost of geometric parameter calculation; and by determining corresponding parameter calculation methods for different base surface geometric types, it can more accurately calculate the geometric parameters of the process feature, thus improving the accuracy of geometric parameter calculation.

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 UAV path planning method based on computational geometry and deep reinforcement learning, constructs a spatial position model of a UAV and an object, takes a bandwidth utilization index, a photo coverage rate and an energy consumption index as evaluation indexes, adopts a reinforcement learning model for training, and then obtains an optimized UAV shooting action and path. The application can significantly improve the UAV shooting efficiency, the object coverage rate of the UAV shooting and the number of redundant photos of the UAV shooting, thereby reducing the bandwidth consumption.

The application discloses a kind of based on node space analysis's identification method of leakage binding steel bar node, binding quality evaluation method, using computer vision identification method, extracts steel mesh node binding state and geometric feature;Then design computational geometry algorithm, analyze the spatial relationship of steel bar node, determine the adjacent position relationship of each steel bar node;Again through plane geometry star topological structure model and optimization algorithm, identify leakage binding steel bar node, calculate qualified binding rate.The method of the application only needs to identify and calculate the information of steel bar node in steel mesh, saves the processing of steel bar section, target detection recognition speed is faster;The computational geometry algorithm and star topological structure model used have higher accuracy and running speed when analyzing the spatial position relationship of steel bar node and identifying leakage binding node.The application greatly improves the construction acceptance efficiency of reinforced concrete warehouse, standardizes the acceptance work flow, improves the informatization and intelligentization in civil construction engineering field.

The invention relates to the technical field of geographic information systems, in particular to a river channel ditch cleaning length standardization automatic calculation method based on computational geometry, which comprises the following steps: loading center line data, left and right shoreline data and ditch cleaning data of a target river channel, merging the center line data, and smoothing to obtain a smooth center line; normal lines are generated on the smooth center line every preset distance, and normal lines intersecting with other normal lines are removed; on the basis of the remaining normal lines, the target river channel is partitioned into a plurality of partition areas, and after long processing, a plurality of partition lines are finally obtained; based on all the partition lines, the target river channel is partitioned into a plurality of closed areas; determining a closed area where two end points of the ditch cleaning line segment are located, and determining projection points of the two end points of the ditch cleaning line segment on the left bank line, the right bank line and the center line; and based on the projection points, the length of the left bank sub-curve, the length of the right bank sub-curve and the length of the center line sub-curve are calculated, so that the final length of the ditch cleaning line segment is calculated.

The present application relates to the technical field of motor design, and particularly relates to a permanent magnet motor iron core size optimization method based on a particle swarm algorithm. The method comprises the following steps: selecting geometric parameters such as stator tooth width to construct particles and initializing a particle swarm; in each round of iteration, an agent model constructed in advance is used to predict the electromagnetic performance index of the particles and to calculate the fitness value; a gradient vector is constructed by perturbing the global optimal solution, and the maximum value of the cosine similarity of the gradient vectors between geometric parameters is calculated as the correlation attribute value; the speed update logic is dynamically corrected in response to the correlation attribute value: an inertia weight inversely proportional to the correlation attribute value is applied to the strongly coupled parameters to suppress oscillation, and an inertia weight proportional to the gradient module length is applied to the weakly coupled parameters to accelerate convergence; until the convergence condition is met, the optimal iron core size is output. The present application effectively solves the oscillation problem caused by strongly coupled parameters, and significantly improves the optimization accuracy and efficiency.

The invention discloses a sparse planting type fruit tree crown adhesion segmentation method based on multi-gradient buffering, and belongs to the technical field of crossing of agricultural remote sensing and geographic information systems, and the method comprises the steps: initializing a working space and parameters, and importing fruit tree crown surface data after semantic segmentation; performing geometric area calculation and screening on the crown surface data, removing small impurities, and distinguishing large adhesion crowns from small independent crowns; performing multi-gradient nested buffering and erasing operation on the large-adhesion tree crowns, and separating adhesion areas; reconstructing a single tree crown surface through line-plane conversion and invalid element elimination; combining single-plant crown surfaces and small independent crowns, and counting the number of fruit trees. The method can efficiently realize single plant separation and accurate plant number statistics of the adhered crowns, provides data support for agricultural insurance claim settlement and yield estimation, is high in operation automation degree, and is suitable for large-scale fruit tree planting areas.

The application discloses an interactive catalytic method and platform based on molecular simulation and artificial intelligence, and realizes intelligent design of a catalyst through data classification, condition optimization, structure modeling and mechanism analysis. After being standardized, the catalytic data uploaded by a user is divided into two categories: data set A is used for recommending optimal reaction conditions by a multi-objective optimization algorithm; and the catalyst structure of data set B is optimized by quantum chemistry software to construct a three-dimensional model, and multiple intermediate, transition state structures and geometric / electronic descriptors are optimized. Through feature screening and dimension reduction technology, a double-model collaborative prediction system of model E and model F is constructed, and finally, model F is used to drive a molecular generation algorithm to screen high-performance catalysts. The application can realize flexible adaptation to specific application scenarios, meet the urgent needs of the industry for efficient and accurate catalyst performance prediction and intelligent design, and effectively help the industry to reduce costs and increase efficiency, and accelerate the catalyst research and development process.

The invention discloses a robot measurement system calibration and uncertainty evaluation method. Firstly, a robot motion error model is established, and an error transmission link is defined; after a theoretical framework is established, N groups of end effector pose data are measured, geometric parameter errors are calculated, and meanwhile, non-geometric parameter errors caused by an approximate external load are experienced. Secondly, estimating discrete probability distribution of related parameters, and obtaining a continuous probability density function and features through approximate fitting; and finally, setting a confidence probability, substituting the confidence probability into the error model to carry out large-scale pseudo-random simulation, obtaining error probability distribution of the end effector, and completing evaluation by combining a measurement mean value and standard uncertainty. According to the method, an uncertainty core source is locked, a parameter influence path is defined, finite sample information is fully utilized, the problems of distribution representation deviation and distortion are avoided, and a precise theoretical basis and reliable data support are provided for uncertainty evaluation of a high-dimensional parameter system.