658 results about "Point set" patented technology

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

The invention discloses a risk assessment and early warning method and system applied to water conservancy project construction, and belongs to the technical field of water conservancy project construction safety. Multi-source data of a construction area is collected, and a multi-dimensional construction information flow set divided according to time, procedures and work points is constructed; constructing a space-time risk causal map based on the construction units, and expressing time sequence association and risk propagation paths among the construction units; mapping the information flow to a graph structure, training a multi-task graph neural network model, and obtaining a risk score and a future risk evolution trend of each construction unit; performing clustering analysis on the risk state, identifying a high-risk work point set, and identifying a risk linkage chain based on a causal map; when the risk score exceeds a dynamic threshold value or a closed propagation structure exists, a grading early warning signal is triggered; according to the method, dynamic identification, trend prediction and intelligent early warning of complex construction risks are realized, and the intelligent level of construction safety management is improved.

A three-dimensional point cloud segmentation method and apparatus based on a locally weighted curvature and a two-point method, which method and apparatus relate to the field of point cloud processing. The segmentation method comprises: calculating a normal vector of each sample point in a three-dimensional point cloud image (S101); calculating a locally weighted curvature of each sample point on the basis of the normal vector, and comparing the locally weighted curvature with a preset curvature threshold value, so as to obtain a comparison result (S102); when the comparison result indicates that the locally weighted curvature of the sample point is greater than or equal to the preset curvature threshold value, marking the sample point as a boundary feature point, so as to obtain a boundary feature point set (S103); fitting a point cloud planar model on the basis of the boundary feature point set, and performing iterative calculation on the point cloud planar model, so as to obtain a target inner point set (S104); and segmenting the three-dimensional point cloud image on the basis of the boundary feature point set and the target inner point set (S105).

The invention relates to the technical field of industrial internet, in particular to a mine network operation and maintenance management system and method for coal mine development, and the system comprises a path passage evaluation module, an equipment state recognition module, a scheduling priority module, an operation path conflict evaluation module and a scheduling task issuing module. According to the method, a weighted time consumption model is constructed through task positions and path section passing difficulty parameters to realize path time consumption dynamic estimation and optimal passing path combination, and equipment state identification adopts operation parameter interval assignment and weighted summarization to realize multi-dimensional risk judgment. In task sorting, time consumption and risk levels are normalized to establish a combination interval to dynamically mark the emergency degree, the scheduling sequence is adjusted in combination with task point distribution, a path point set intersection is extracted in path conflict processing, the proportion of overlapped sections is calculated, and task positions are adjusted according to a threshold value to guarantee scheduling continuity; and task issuing uniformly generates a scheduling instruction set containing a path equipment state and a task type, so that the scheduling precision and the execution consistency are improved.

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention provides a power cable comprehensive online monitoring system, and relates to the technical field of data processing, and the system comprises the steps: extracting a partial discharge distribution point set in a multi-dimensional feature fusion data set, and calculating the weight factor of each distribution point; based on the weight factors, a weighted point set concave packet generation algorithm is adopted to construct a partial discharge source probability distribution boundary, and a boundary feature point set is obtained; inputting the boundary feature point set and the multi-dimensional feature fusion data set into a convolution-long and short-term memory hybrid neural network to generate a diagnosis result; and a third-level early warning signal is generated based on a diagnosis result, early warning information is pushed to a remote operation and maintenance terminal and a mobile APP through an MQTT protocol, a digital work order service is automatically triggered to generate a maintenance task, and positioning verification is executed. According to the invention, the accuracy of monitoring and fault diagnosis is improved.

The invention discloses a hybrid model power prediction method fusing an improved snow ablation optimization algorithm and CNN + KAN + LSTM + MSA. The method comprises the following steps: constructing a snow ablation optimization algorithm according to a fusion good point set strategy and a periodic oscillation mutation strategy; and a CNN-KAN + BiLSTM-MSA hybrid model fused with the GVSAO algorithm and a hyper-parameter learning process are constructed. Through a good point set strategy, the initial solution distribution is more uniform, the coverage solution space is wider, and the global search capability is enhanced; the periodic oscillation mutation strategy prevents premature convergence of the solution, keeps diversity of the solution, and ensures exploration of the globally optimal solution; in the local development stage, candidate solutions are refined step by step, and the understanding quality and convergence efficiency are improved; the performance of a snow ablation optimization algorithm is greatly improved, and a global optimal solution can be better found; spatial feature extraction and time sequence learning are combined, so that the problem of time sequence prediction is effectively solved.

The invention provides a grouting simulation method and system based on time sequence interaction and self-adaptive grid reconstruction, and relates to the technical field of grouting engineering.The grouting simulation method comprises the steps that monitoring data of the grouting construction process are obtained, the monitoring data are input into a grouting diffusion prediction model, a grouting pressure prediction value is output, and a series of prediction responses are conducted based on the grouting pressure prediction value. Obtaining a grouting pressure predicted value, a boundary point set of the grouting diffusion area, the area and the volume of the grouting diffusion area and a diffusion response result of the pressure distribution diagram; constructing a geometric model of the grouting area based on the diffusion response result, and performing grid unit division and grouting diffusion area mapping on the geometric model; and performing local grid encryption and reconstruction on the grid units according to a density control rule to obtain a structured grid data model, adaptively updating the structured grid data model by using real-time monitoring data, and performing real-time visual grouting optimization control. According to the invention, the high-precision prediction response of grouting is improved.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

Disclosed are free-form curved surface slicing method and device based on implicit model, includes following: (1) Using mesh model of layered curved surface to intersect with implicit model of a part to be manufactured to obtain contour lines; (2) Comparing rules for intersecting contour lines and meshes within mesh, mesh coordinates are substituted into implicit model, obtaining a position of an intersection point of contour lines in corresponding mesh; (3) Using interpolation method to calculate intersection coordinates of contour lines and mesh boundary, specifying storing sequence of interpolation points, storing intersection points of discrete line segments in mesh; (4) Merging unordered point sets obtained to establish topological relationship between point sets and discrete line segments, each line segment corresponds to two endpoints, each endpoint corresponds to two line segments, based on above, all points traversed will be connected into complete and ordered profile, that is sliced profile.

The invention discloses a BIM-based construction engineering visual management method and system, and relates to construction engineering management, and the method comprises the steps: collecting a data set containing geometric information, attribute information and associated information; dividing the data set into a plurality of hierarchical subsets according to the hierarchical relationship of the building engineering BIM model; an improved DBSCAN clustering algorithm is adopted to perform clustering analysis on each hierarchy subset; classifying and archiving the data set according to the data cluster, and generating a semantic association data collaboration array A according to a classifying and archiving result; carrying out dimension reduction processing on the semantically associated data collaboration array A; constructing a spatial index of the dimension reduction data array by adopting a density-based spatial index method; and dividing the building engineering BIM model into a plurality of partitions by using a distributed computing framework, and performing distributed matching on the partitions and the spatial index to obtain a data matching point set. For the problem that the BIM-based building engineering high-dimensional data clustering precision is low in the prior art, the analysis precision is improved.

The invention relates to a comprehensive pipeline collision detection method and system, and the method comprises the steps: obtaining a three-dimensional model of a pipeline, and carrying out the self-adaption segmentation according to the bending degree if the pipeline is a bent pipe; extracting the central axis of the pipeline, determining the density of the pipeline according to the pipeline distribution and complexity, if the pipeline distribution is sparse, establishing a capsule body by using the central axis, and screening out the pipeline which may collide through coarse collision detection according to the capsule body; if the pipelines are densely distributed, screening out the pipelines which may collide by combining a bounding box based on a spatial index method; a certain number of points are sampled on the surfaces of the two possibly collided pipelines according to the curvature and complexity of the pipelines to obtain two point sets, whether collision points exist or not is determined according to distribution of elements in the two point sets, and if the collision points exist, the collision points are displayed in the three-dimensional model of the pipelines.

The invention provides a water-based adhesive coating control method and system based on artificial intelligence optimization, and the method comprises the steps: obtaining a historical data set composed of process parameters of a coating process and coating quality parameters, carrying out the clustering, and obtaining a global induction point set according to a clustering result; determining a stage induction point subset according to the target parameter value of the current coating stage and the boundary of the target parameter, and updating the sparse Gaussian process regression model by using the global induction point set and the stage induction point subset; calculating a quality fluctuation index, determining the length of a prediction time domain based on the quality fluctuation index, constructing an optimization problem in the prediction time domain by adopting a multi-level opportunity constraint mode, and when the deviation value between the actual value of any key process parameter and the prediction trajectory based on the sparse Gaussian process regression model exceeds a deviation threshold value, determining that the prediction trajectory does not exceed the deviation threshold value. And solving the optimization problem to obtain an optimal control action sequence in the prediction time domain, and determining a final control action from the optimal control action sequence and sending the final control action to an execution mechanism.

The invention discloses a pose estimation method, computer equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a current frame, and performing feature point extraction on the point cloud data to obtain at least two types of feature points; performing region division on the feature points of each category to obtain at least two categories of feature points corresponding to each region; clustering processing is carried out on the feature points, corresponding to the areas, of the categories to obtain centroid point sets corresponding to at least two categories; and performing pose estimation by using the centroid point sets corresponding to the at least two categories to obtain the current pose of the target equipment. According to the scheme, the accuracy of pose estimation can be improved.

The invention discloses a special-shaped shutter fragment splicing drawing method based on space coordinates, and relates to the field of intelligent construction.According to the method, a laser radar scanning device is used for obtaining a window face initial point cloud, and three-dimensional point set representation under a local space coordinate system is established; then executing a multi-level boundary point extraction process including boundary edge identification, mutation point detection and structure segmentation, and constructing a candidate boundary point set Bpre; on the basis, a five-level space structure partitioning strategy is applied, partitioning point distribution rules are set for regions with different geometric characteristics, and finally a window boundary point set B is formed; the point set has high adaptability in the aspects of structural continuity, spatial distribution density, sampling precision and boundary integrity, and the geometric reduction degree of subsequent splicing modeling is remarkably improved; according to the scheme, the problem that a traditional method is difficult to process local complex curvature and structure jump of a special-shaped window is solved, and continuous mapping of structure boundary information to spliced pattern construction is achieved.

The invention discloses a welding seam self-adaption B spline curve fitting method for a 3D dense out-of-order point set, and aims at solving the problem of complex curved surface welding seam track generation in robot welding and breaking through the limitation of the prior art through a three-stage self-adaption fitting mechanism. The method comprises the following steps: firstly, self-adaptive screening and projection sorting are carried out by utilizing a straight line fitting error, so that the disordering problem of discrete points of a quadratic curved surface intersection line is effectively solved, and the strong dependence of a traditional method on a CAD model is overcome; secondly, in combination with a region growing method guided by an end tangent vector, robust conversion from an out-of-order point set to an ordered control point is realized through dynamic neighborhood search and projection value sorting, and path continuity of an open / closed curve is guaranteed; and finally, mean sampling quadratic fitting is adopted, control point distribution is optimized while noise is restrained, and a smooth B-spline track meeting the movement requirement of the welding gun is generated. According to the method, direct fitting of the 3D dense out-of-order point set under the model-free condition is achieved for the first time, and the generation efficiency and adaptability of the irregular welding seam track are remarkably improved.

The embodiment of the invention provides an underground pipeline extraction method, system and device based on point cloud and a storage medium, and belongs to the technical field of point cloud space geometry extraction. According to the scheme, underground pipeline point cloud data are obtained; segmenting the pipeline point cloud data to obtain a segmentation result; when the segmentation result is a single-pipe scene, principal component analysis and cylinder fitting are carried out based on all pipeline point cloud data to obtain a normal vector and a pipeline radius of each data point; performing plane cutting according to the normal vector to obtain a tangential plane, and obtaining a pipeline center line based on the tangential plane; determining a distance threshold parameter according to the radius of the pipeline; based on the distance threshold parameter, performing segmented straight line fitting on the pipeline center line through a random sampling consistency algorithm to obtain a center line point set; performing feature extraction on the center line point set to obtain feature points; determining a connection relationship of adjacent feature points according to the geometrical relationship; the pipeline reconstruction model obtained based on the connection relation can be suitable for complex scenes, and the calculation efficiency is improved.

The invention relates to a design optimization method based on big data analysis, and the method comprises the following steps: carrying out the improved point recognition of clustering analysis features through an improved point recognition algorithm, obtaining an improved point set, carrying out the primary optimization of a preliminary design scheme according to the recognized improved point set, and obtaining a first optimization design scheme; predicting the market demand of the first optimization design scheme to obtain a market demand prediction result; performing value evaluation on the market demand prediction result to obtain a value evaluation result; according to the method, association rule mining is carried out on a first optimization design scheme to obtain an improved strategy, and secondary optimization is carried out on the first optimization design scheme according to the improved strategy to obtain a second optimization design scheme, so that the problem that most traditional methods are lack of systematic association rule mining and improved strategy generation mechanisms is solved; the technical problems that in the iteration process of design optimization, key improvement points cannot be accurately recognized, the optimization process is delayed, and the development cost is increased are solved.

The invention discloses a dragging positioning spraying track planning method for a local area of the surface of special equipment and a storage medium, and belongs to the technical field of industrial robot automation. The method comprises the steps of obtaining point set data of a to-be-sprayed area; calculating a convex hull based on the point set, and solving a minimum enclosing rectangle by using a rotary caliper method to determine an accurate rectangle spraying boundary; constructing a rectangular amplitude limiting sedimentary model through spraying testing and parameter identification; generating a grid track with a buffer area based on the model and the boundary, and optimizing the spraying speed by taking the minimization time as a target; and for the curved surface, performing B-spline curved surface reconstruction and extracting a normal vector to control the attitude of the spray gun. According to the method, the complex multi-parameter trajectory optimization problem is simplified into single-speed optimization through the rectangular amplitude limiting deposition model, the problems that in local spraying, trajectory planning is complex, edge control is not precise, a coating is not uniform and the like are effectively solved, and the spraying efficiency and quality are remarkably improved.

The invention relates to the field of assembly automation and flexible manufacturing, and discloses an assembly method for a prefabricated firewall of a transformer substation, and the method comprises the following steps: setting a plurality of space positioning reference points at the edge of a wallboard; acquiring three-dimensional coordinate data to form a first point set and a second point set; calculating an initial rigid body transformation parameter between the wallboards according to the first point set and the second point set; establishing an energy functional for describing a total error; carrying out minimization solution on the energy functional by adopting a variational method to obtain an optimal disturbance vector field; according to the optimal disturbance vector field, a plurality of fine adjustment actuators of the wallboard are controlled, posture fine adjustment is conducted, and physical assembly connection of the wallboard and the target wallboard is completed. According to the invention, accurate attitude estimation and error correction in the wallboard assembly process are realized; the robustness and geometric fitting capability of the assembly process are improved; the assembly precision and efficiency are remarkably improved, and the problem that traditional redundancy control distribution is unstable is solved.

The invention discloses a dual-path three-dimensional point cloud compression reconstruction method fusing multi-scale geometric features. The method comprises the following steps: extracting representative skeleton anchor points from original point clouds by adopting farthest point sampling; predicting an octree node occupancy state by using an anchor encoder, and performing entropy coding to generate a skeleton bit stream; a skeleton condition feature encoder is introduced, probability distribution of local features is predicted by using skeleton context priori, cross-path context association is constructed, and a feature bit stream is generated; in the decoding stage, the sparse skeleton point set is restored from the skeleton bit stream, local features are recovered from the feature bit stream in combination with the skeleton structure context, fine-grained reconstruction is carried out on the point cloud through a geometric fusion point cloud up-sampling module, and finally the high-fidelity point cloud is obtained. According to the invention, the point cloud compression effect and reconstruction quality can be improved. The method can be widely applied to the point cloud field.

The invention discloses a component-level digital twin railway tunnel engineering three-dimensional model three-dimensional tile conversion method, which comprises the following steps: opening a tunnel BIM model file, and obtaining external setting parameters, artificial constraint parameters and model information to generate triangular mesh model data; establishing an association relationship between the semantic attribute information and the triangular mesh model, and mapping one by one to obtain LOD4 hierarchical model data; screening and cleaning the semantics, and removing a specified component to obtain LLO3 hierarchical model data; obtaining a maximum outer bounding box fitting elliptic cylinder of the tunnel model, and performing similar block replacement to obtain LOD2 hierarchical model data; the method comprises the following steps: traversing sprayed concrete members according to a line trend sequence to obtain coordinates of geometric center points of bounding boxes of the sprayed concrete members, and forming an ordered point set fitting line as an LOD1 hierarchical model; performing format conversion on the LOD models of different hierarchies, and finally organizing the LOD models into a tree structure to form three-dimensional tile structure data; the method is simple and high in execution efficiency.

The invention belongs to the technical field of point cloud data processing, and particularly relates to a robust point cloud registration method and device based on common-view query embedding. The method comprises the following steps: using KPConv-FPN as a feature encoder to carry out multi-scale sampling on an input point cloud pair and extracting point-by-point features; inputting the thickest sampling point and the features of the thickest sampling point into an inter-block common-view feature interaction module, and carrying out common-view feature interaction and common-viewpoint segmentation; inputting the enhanced features and common viewpoints into a block matching module with filtering so as to extract a corresponding relation under a coarse scale; on the basis of a rough matching result, inputting the upper sampling points and the features thereof into a point matching module to generate a dense corresponding point set; calculating a group of transformation candidates by using the corresponding point sets; and finally, registering by using a local-to-global registration scheme. According to the method, the problems of high training cost, extra calculation overhead and low overall efficiency of point cloud registration caused by a traditional two-stage design are solved.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention discloses a tool path and blank cutting volume calculation method based on point set representation, and the method converts a blank and a tool path into point sets, calculates the cutting volume through the intersection of the point sets, and simplifies the traditional geometric calculation. Meanwhile, the density of the point set can be flexibly adjusted, the calculation efficiency and precision of tool path optimization are improved, and dependence on an STL file and a third-party plug-in is avoided. The method is suitable for large-scale tool path optimization tasks, and the flexibility and controllability of the tool path optimization process can be improved.

The invention discloses an IGBT module temperature anomaly identification method and device, and the method comprises the steps: obtaining a temperature time sequence, and carrying out the normalization of the temperature time sequence, and forming an original data set; performing time sequence prediction based on the original data set to obtain a prediction value of each moment, and calculating a residual term of the prediction value and an original value in the original data set to form a residual data set; determining a weighted average distance of each residual item according to the K-nearest neighbor point set of each residual item and a preset weighting function; according to a preset value range of the minimum point number, determining an optimal minimum point number by calculating the minimum value of the clustering evaluation index; and performing clustering analysis on the residual data set based on the weighted average distance and the optimal minimum point number, and identifying the abnormal temperature according to a clustering analysis result. The embodiment of the invention does not need to manually set a fixed threshold value, can adapt to temperature changes in different environments and working conditions, and has better flexibility.

The invention discloses a data processing method and device, computer equipment and a cluster, and relates to the field of computers. A plurality of sliding windows can obtain a plurality of tangency points by processing the to-be-stored data at a time, and the calculation time consumed by the calculation device for obtaining the tangency points is shortened. If the number of the sliding windows is too large, the multiple sliding windows slide in parallel to search for the tangency points of the to-be-stored data, the storage space of the cache may be insufficient, and the cache occupancy rate is high, or the multiple sliding windows slide in parallel to determine multiple tangency points, and the calculation time consumption of the tangency points may be long; if yes, the computing device adjusts the degree of parallelism according to at least one of the computing consumed time of the first tangent point set and the cache occupancy rate so as to adjust the number of sliding windows, so that the computing consumed time of tangent points is reduced, cache occupancy is reduced, reasonable tangent points of the to-be-stored data are obtained as soon as possible, and the length of data blocks selected by the multiple sliding windows is adjusted; the accuracy of the computing device for acquiring the tangent point is improved, the acquisition rate of data repeated with the stored data in the to-be-stored data is improved, and the data deduplication rate is improved.

A robot interference checking motion planning technique using point sets. The technique uses CAD models of robot arms and obstacles and converts the CAD models to 3D point sets. The 3D point set coordinates are updated at each time step based on robot and obstacle motion. The 3D points are then converted to 3D grid space indices indicating space occupied by any point on any part. The 3D grid space indices are converted to 1D indices and the 1D indices are stored as sets per object and per time step. Interference checking is performed by computing an intersection of the 1D index sets for a given time step. Swept volumes are created by computing a union of the 1D index sets across multiple time steps. The 1D indices are converted back to 3D coordinates to define the 3D shapes of the swept volumes and the 3D locations of any interferences.

The present application discloses a method, device, storage medium and electronic device for determining a point trace set. The method includes: obtaining a first point trace set collected by the vehicle object in the current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; the first number of first point trace objects included in the first point trace set, and a point trace processing identifier matching the historical point trace set collected in the previous frame, and when the target processing conditions are met, obtaining the first processing description parameter and the second processing description parameter matching the historical point trace set; determining the number of target point traces according to the first processing description parameter and the second processing description parameter; screening the first point trace object in the first point trace set according to the number of target point traces to obtain the second point trace set. The present application solves the technical problems of inaccurate determination of point trace sets and low efficiency in the related art.