122 results about "Reverse modeling" patented technology

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention provides a geophysical gravity and magnetic anomaly source model construction and rapid forward and reverse modeling method, and relates to the technical field of intelligent three-dimensional geological modeling and simulation. According to geological information of a research area, source body modeling parameters of a gravity and magnetic anomalous field are set for the research area, and a three-dimensional model matrix M capable of describing a plurality of underground anomalous bodies is obtained; constructing a fast forward modeling network, establishing nonlinear mapping from the three-dimensional model matrix to the simulated abnormal data through the fast forward modeling network, and calculating forward modeling response of the three-dimensional model matrix; and constructing an inversion network based on a concurrent module CFTBlock and combining a CNN and a Transform, and establishing nonlinear mapping from the gravity and magnetic abnormal data to a three-dimensional model matrix. The method has the advantages of fast and accurate forward modeling, high-resolution inversion and the like, and is suitable for better interpretation of actually measured gravity and magnetic data.

The invention discloses an adaptive edge detection iterative denoising point cloud reconstruction method and system. A regularization parameter and a kernel bandwidth are automatically set through point cloud normalization and local covariance noise estimation; an implicit curved surface is fitted based on a parameterized Gaussian field, and initial curved surface construction without a normal direction is realized. Edge detection is carried out by utilizing Voronoi covariance measurement, and the edge detection is continuously mapped into a projection weight, so that feature details of the point cloud are reserved in an acute angle region, and smooth denoising is realized in a flat region. The regularization intensity is dynamically adjusted through residual trend analysis to enhance detail representation, and a triangular mesh is extracted and output in combination with octree discretization and contour surface extraction. The method has unsupervised adaptivity, has outstanding performance in the aspects of edge fidelity, noise robustness and complex structure adaptability, can effectively process point cloud data with high noise, missing normal direction or irregular structure, and is suitable for the fields of engineering survey, reverse modeling, cultural relic digitization and the like.

The invention provides a reverse modeling design method for a passive device and a computer readable medium, and the method comprises the steps: generating samples of different layout sizes of the passive device, and building a performance data set; establishing and pre-training by taking the geometric parameters and the working frequency as input parameters and the performance parameters as output parameters to obtain a forward model; constructing a reverse neural network model; dividing a parameter space of the whole performance data set into a plurality of sub-training domains, and respectively establishing and training a reverse sub-model in each sub-training domain; after training is completed, all the reverse sub-models are integrated to form a final reverse neural network model, and the reverse neural network model is used for outputting geometric parameter solutions meeting conditions according to the specified working frequency and performance parameters. According to the method, a high-confidence data set is constructed by using a forward model, and then the data set is divided based on the uniqueness of output parameters and the reverse sub-models are obtained respectively, so that rapid and accurate mapping from target performance parameters to geometric parameters is realized.

The invention discloses an underground medium multi-scale forward and reverse modeling method and system based on a decoupling neural network, and belongs to the technical field of underground multi-physics field coupling. The underground medium multi-scale forward and reverse modeling method based on the decoupling neural network comprises the following steps: generating a spatio-temporal evolution data set of multiple physical field variables; a physical field decoupling physical information neural network architecture is constructed, the architecture comprises three special sub-networks, each sub-network takes time-space coordinates as basic input and dynamically receives output of other sub-networks as auxiliary input features, and strong coupling modeling between physical fields is realized through feature sharing and a joint loss function; executing a two-stage training strategy; and executing forward intelligent prediction or key physical property parameter inversion based on the trained neural network architecture. According to the method, a unified neural network framework composed of a plurality of special sub-networks is constructed, physical consistency and data-efficient cross-scale modeling from a rock core scale to a site scale are realized, and forward simulation and parameter inversion are synchronously supported.

The invention provides a method and a device for installing, positioning and monitoring a laminated slab in a beam. Relates to the technical field of building construction. According to the specific implementation scheme, the method comprises the steps that reverse modeling is conducted on a prefabricated laminated slab component through laser scanners placed at multiple target positions, and a point cloud model is generated; inputting preset data of the prefabricated laminated slab component into the building information model to generate a target preset model; and the point cloud model is matched with a target preset model, and when it is detected that the matching degree of the point cloud model and the target preset model is smaller than a first preset threshold value, multiple installation point positions for beam-entering installation of the prefabricated laminated slab component and normal state parameters of the multiple installation point positions are determined based on the point cloud model and the target preset model. According to the technical scheme, the accuracy and construction efficiency of installation and positioning of the laminated slab into the beam can be improved, structural hidden dangers caused by installation dislocation are avoided, it is guaranteed that the laminated slab and the beam form a reliable overall stress system, and the stability and safety of a building structure are enhanced.

The invention discloses a soft tissue constitutive parameter calibration method in a soft tissue-skeleton bird body model for bird strike simulation, and the method comprises the construction of a soft tissue-skeleton bird body finite element model and finite element reverse analysis based on flat bird strike. Dividing wing bones, leg bones and head and neck parts in the bones of the bird body into hexahedral unit grids, dividing wishbones, pecking bones, fossil protrusions and ribs into shell units, and dividing soft tissues into SPH particles; further obtaining bone elastic-plastic constitutive parameters through a static compression test, and endowing the bone units with the bone elastic-plastic constitutive parameters; an objective function is set as a mean square error between a flat plate center point pressure-time curve obtained by a real bird body impact flat plate test and corresponding curve data calculated by soft tissue-skeleton bird body impact flat plate finite element simulation, and soft tissue constitutive model parameters are inversely calibrated by adopting a genetic algorithm; and model and material parameter support is provided for bird strike simulation of a high-fidelity aircraft structure.

The invention discloses a reverse BIM modeling method, system and equipment for an existing building space rod system composite structure, and belongs to the technical field of BIM modeling. In order to solve the problem that in the prior art, point cloud processing software lacks a special recognition and reconstruction tool for a'ball node-rod piece 'class space rod system combined structure, and efficient and accurate reverse generation of a rod piece axis is difficult to achieve, through curvature analysis, DBSCAN clustering, RANSAC spherical surface fitting and other technologies, the position of a ball node is accurately recognized from actually measured point cloud data; and finally generating a high-precision BIM model of a space rod system combined structure according with engineering practice by combining a two-stage geometric filtering strategy according to a sphere center connecting line of sphere nodes and the space constraint of the rod piece point cloud data. According to the method, the problem that actually measured point cloud data is discrete and difficult to process is effectively solved, and reliable technical support is provided for health monitoring, digital filing and accurate modeling of the existing building space rod system composite structure.

The invention aims to provide a high-precision design and optimization method for an EMI (Electro-Magnetic Interference) filter aiming at the corresponding defects in the prior art, a mixed neural network architecture model is constructed by combining a feedforward neural network and a convolutional neural network, and the filter is subjected to reverse modeling by utilizing the mixed neural network architecture model, so that the filtering precision is improved. According to the method, the predicted value, the topological structure and the element parameters of the filter are obtained, then the element parameters are subjected to multi-objective optimization by using a multi-objective genetic algorithm to obtain the optimized element parameters, and the optimized element parameters and the topological structure of the filter jointly form an optimization scheme of the filter, so that the design efficiency and the optimization precision of the EMI filter can be remarkably improved; and the design period of the EMI filter is shortened, and the market demand of rapid iterative updating of current high-frequency electronic equipment can be met.

The invention provides a time sequence prediction task-oriented basic model construction method, which comprises the following steps of: constructing a Patch division layer which is used for cutting an input time sequence by adopting a context window adaptive mechanism combining time domain and frequency domain statistical characteristics to generate overlapped time periods; an embedded layer is constructed and used for converting the processed time sequence into embedded representation and generating an embedded Token through linear projection processing; constructing a forward and reverse frequency domain enhanced bidirectional state space module which is used for outputting forward and reverse modeling results by taking the input sequence and the time reversal sequence thereof as input, and fusing the forward and reverse modeling results to obtain a final fusion result; constructing a feed-forward layer which is used for taking the final fusion result as input to encode a time dependency relationship; and constructing an output and projection layer for mapping the coded time sequence to a predicted future value through linear projection. The method can adapt to various time series data under zero sample setting, and has good modeling robustness and cross-variable modeling capability.

The invention discloses a point cloud-based reverse modeling curved surface generation method and system, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining three-dimensional point cloud data of a to-be-processed engineering design product, and projecting the three-dimensional point cloud data to a preset cutting plane to obtain a two-dimensional projection point set; and performing dimension reduction processing on each projection point, calculating a local geometrical relationship between the projection point and a neighborhood point, and mapping the local geometrical relationship to a one-dimensional parameter space to obtain a one-dimensional parameter value. Clustering analysis is conducted on the two-dimensional projection point set based on the one-dimensional parameter values, the two-dimensional projection point set is divided into a plurality of point cloud clusters, and each point cloud cluster corresponds to one geometric feature curve. And sorting the projection points in each point cloud cluster according to a spatial position relationship to obtain an ordered point sequence. And identifying the geometric feature type of each ordered point sequence, performing curve fitting, and extracting parameter information to obtain the feature parameters of the engineering design product. And generating an accurate curved surface model according to the characteristic parameters. By implementing the method, the accuracy of point cloud clustering can be improved.

The invention relates to a flat shell and an efficient printing method thereof, and belongs to the technical field of additive manufacturing. According to the invention, the axis of the flat shell is parallel to the substrate, and the support is arranged between the flat shell and the substrate; the substrate is divided into a region A and a region B; the area A is quadrilateral and is located in the center of the substrate, the side length of the area A is 55-65% of the side length of the substrate, and the area B is the remaining area obtained by subtracting the area A from the substrate; through analogue simulation, pre-printing and local reverse modeling, the deformation of the area A is controlled within + / -2mm; the deformation amount of the area B is controlled within + / -3 mm; the area A and the area B adopt different printing parameters, and the laser power and the scanning speed of the area A are both higher than those of the area B; and carrying out heat treatment to obtain the flat shell. And meanwhile, it is guaranteed that the deformation of the obtained flat shell is smaller than or equal to + / -0.5 mm, the tensile strength is 1000 MPa or above, and the yield strength is 800 MPa or above.

The invention discloses a deepwater bank protection unit row construction error reverse modeling correction method and device, and relates to the field of data processing. Obtaining water body profile data of a target construction area where the deepwater bank protection unit row is located; according to the water body profile data, constructing a hydrological state tensor; inputting the hydrological state tensor into the prediction correction model, and constructing a layered water resistance field and a non-uniform sinking path dynamic response equation of the unit drainage body; determining a prediction result based on the stratified water resistance field and the non-uniform sinking path dynamic response equation; obtaining an attitude response vector flow sent by the sensor equipment after the unit row body enters water; dynamically matching the attitude response vector flow with a prediction result, and outputting an arrangement path correction value and a pitching attitude adjustment suggested amount; and dynamically correcting the attitude and the sinking path of the unit row body based on the arrangement path correction value and the pitching attitude adjustment suggested amount. The construction precision of the deepwater revetment unit row in a complex hydrological scene can be improved conveniently.

The invention discloses a large metal thin-wall component manufacturing method based on stress distribution state management, which comprises the following steps of: aiming at the configuration of a large metal thin-wall component to be manufactured, constructing a manufacturing process combining reverse modeling analysis and forward process design; the reverse modeling analysis starts from the working step at the tail end of the manufacturing process, the stress distribution state between the working steps serves as a transmission and control element, and in the direction from the tail end to the front end, each working step proposes the final stress distribution state meeting the most simplified requirement of the manufacturing scheme of the working step for the previous working step; and based on the result of the reverse modeling analysis, on the basis of the final stress state determined by the previous step, the tool and the forming process of each step are determined, so that the final stress distribution state of the component manufactured by the step meets the requirements of the next step. The problems that the forming precision of a large metal thin-wall component welding component is poor, the precision coordination consistency of all procedures is poor, and the component manufacturing quality is difficult to guarantee are solved.

The invention discloses a reverse modeling method and device for rapidly evaluating and counting turbine blade profile deviation and a medium, and the method comprises the steps: obtaining coordinate data of a limited section through three-coordinate measurement, and processing key areas such as a film hole and a trailing edge in a data processing stage to form measurement point data of a complete section; according to the multiple pieces of section coordinate data, curved surface modeling is conducted through a curved surface reconstruction mode fitting method, and a complete machined blade shape is obtained; comparing the actually processed blade with the designed blade to obtain the deviation between the two blades, and finally obtaining the deviation of the processing model; the key parameters are adjusted through blade grid modeling software to be fitted with the machined blade, and then the variable quantity of the key design parameters is obtained; through statistics of batch blades, the change range of key design parameters of the blades is obtained, a machining deviation blade profile is generated, and a turbine cooling blade entity is generated, so that pneumatic, heat transfer and strength analysis is carried out according to a turbine blade entity model sample.

The invention provides a building point cloud high-precision automatic registration method based on multi-source sensor fusion, and relates to the field of data processing. According to the method, building point clouds are obtained through multi-source sensor fusion, a time sequence and coordinates are unified, noise and transient targets are removed through density distribution and an occlusion relation, a three-state point cloud label is generated, rough registration and global and local joint optimization are carried out in combination with semantic classification and a cross-scale semantic association graph, and a high-precision registration result is obtained. And on this basis, dynamic difference labeling is executed to generate a component life cycle map, a result is mapped to a building information model, reverse modeling data with life cycle attributes and an engineering application data set are output, and high-precision modeling and application of the whole construction process are achieved. By implementing the technical scheme provided by the invention, high-precision automatic registration of the multi-source point cloud data is facilitated, so that the data set which can directly serve building information modeling and engineering application is mapped.

The invention relates to an intelligent green curtain wall reverse modeling method based on BIM-IoT fusion in the technical field of data processing and artificial intelligence. Comprising the following steps: acquiring BIM component data and IoT sensing data, and constructing a data set; performing node feature fusion and edge weight calculation based on the data set, and constructing a dynamic graph structure; constructing a geometric parameter prediction model based on machine learning, wherein the geometric parameter prediction model comprises a spatio-temporal feature encoder module, a spatio-temporal diagram convolutional network module, a multi-scale feature pyramid module and a physical constraint decoder module; inputting the dynamic graph structure into a geometric parameter prediction model based on machine learning, and carrying out iterative training; and constructing a real-time dynamic graph structure based on the basic BIM component data and the real-time IoT sensing data, inputting the real-time dynamic graph structure into the trained geometric parameter prediction model based on machine learning, outputting a predicted geometric parameter vector, and performing intelligent green curtain wall maintenance. And cooperative expression of static geometry and transient environment change of the component is realized.

The invention relates to a steel structure curved surface curtain wall mounting method based on a reverse modeling technology, which comprises the following steps of: performing spatial scanning on a steel structure to obtain a three-dimensional point cloud data set covering the geometric morphology and spatial position information of a curved surface; reverse modeling is carried out based on the three-dimensional point cloud data, and a three-dimensional digital model highly matched with the steel structure is constructed; the model is corrected; carrying out curtain wall design optimization based on the final model, and generating a digital processing drawing; and deepening the digital processing drawing of the curtain wall component, and carrying out field installation operation of the curtain wall component. According to the method, through space scanning and reverse modeling, secondary optimization is carried out on design and installation of the curtain wall based on a reverse modeling result, and by means of on-site curtain wall installation precision control and the like, the engineering problem of curtain wall installation of a complex steel structure is solved, and the measurement precision of the curtain wall in the early stage of installation is improved. The installation quality and efficiency of the curtain wall are improved, the cost is reduced, the construction period is shortened, and finally the aesthetics and functionality of a building are improved.

The invention relates to the technical field of building information data analysis, in particular to a building structure intelligent reverse modeling and analysis system based on BIM, and the system achieves the denoising of the point cloud data of a building structure through a data collection module, a downsampling module, a structural similarity classification module, a data denoising module and a model construction module. And completing the construction of the building structure BIM model. The system analyzes the minimum point cloud data selected in the RANSAC fitting process by using the reproduction structure in the building structure based on the reproducibility of the building structure, so as to determine the weight of each point cloud data belonging to a priori interior point in the point cloud set selected by each fitting, and distribute a corresponding error threshold to the weight, thereby screening out noise points, and improving the robustness of the system. The accuracy of a reverse modeling structure can be effectively improved, and the robustness of the model to noise is further improved.

The invention relates to the technical field of computer graphics and reverse engineering, in particular to a ship workpiece point cloud reverse modeling method based on OpenCasCad, which comprises the following steps: collecting ship workpiece point cloud data through laser 3D scanning; preprocessing the point cloud by adopting uniform sampling and radius filtering; segmenting the point cloud into a bottom surface point cloud and a top surface point cloud through slicing processing and region growth, and extracting contour data containing vertex data and normal vectors; constructing a unique bottom plane based on bottom surface contour data fitting; projecting the top surface and the bottom surface to the bottom plane, carrying out gap splicing on the top surface, and fitting a minimum external contour on the bottom surface; and stretching the top surface along the reverse direction of the normal of the bottom plane, cutting the stretched entity by using the original top surface, and splicing with the bottom surface to form a complete model. According to the method, the problems of non-coplanar contours, inclined plane deviation, gap defects and the like in the prior art are solved, the modeling precision and efficiency are improved in combination with OpenCasCad, and the method is suitable for reverse modeling of complex workpieces of ships.

The invention discloses a tailings pond water treatment dosage prediction method based on historical output reverse modeling, comprising: acquiring a historical data set, the historical data set comprising water outlet end water quality index data and an actual dosage, the water outlet end water quality index data comprising a plurality of water quality indexes, the plurality of water quality indexes at least comprising a wastewater flow, a water outlet end water quality forward prediction model based on the GRU neural network is constructed, the water outlet end water quality forward prediction model obtains a water outlet end water quality prediction value based on the historical data set as input, and a calculation function of the dosage adjustment amount is constructed based on the difference value between the water outlet end water quality prediction value and preset target water outlet end water quality index data; and taking the dosage adjustment amount meeting specific conditions in the calculation function as the final dosage adjustment amount, and determining that the dosage at the next sampling moment is the last dosage plus the final dosage adjustment amount. According to the invention, real-time or quasi-real-time dosage prediction is realized.

The invention discloses a reverse modeling method for an S parameter of a microwave power transistor based on a bidirectional long short-term memory neural network-long short-term memory neural network (BiLSTM-LSTM) technology, and the method comprises the five steps of data preparation, model design, hyper-parameter setting, model training and optimization, and model evaluation. According to the BiLSTM-LSTM modeling method, the core is that forward and backward and context features of data are extracted through a BiLSTM layer, time sequence dependence modeling is carried out in combination with an LSTM layer, and therefore the nonlinear mapping relation between performance parameters and S parameters of a microwave power transistor is fully excavated. According to the method, the spatial-temporal characteristics and relevance of data can be effectively captured, the precision and generalization ability of a transistor model are remarkably improved, and an efficient and accurate modeling method is provided for realizing high-performance and high-reliability circuit design.

The invention discloses a digital method for accurate duplication, transfer and verification of an in-place track, and belongs to the technical field of oral cavity restoration digitization. The method comprises the following steps: carrying out in-place path observation of a digital model in oral rehabilitation computer-aided design software, and generating an undercut filling model along the observation direction; constructing a parameterized three-dimensional feature model in reverse modeling software according to the undercut filling model; and automatically identifying the projection contour of the feature model through an algorithm, calculating the deviation angle between the current observation direction and the target in-position track, and guiding the model to observe and adjust. According to the invention, a track consistency verification system is constructed, accurate copying and transferring of the track direction are realized in a digital mode, the system can be adapted to dental restoration computer-aided design software with different function configurations, and a standardized and verifiable track digital transmission scheme is provided for clinical diagnosis and treatment of dental restoration.

The invention relates to a rotating objective table for ultra-micro-distance photography and a 3D reverse modeling image acquisition method, the rotating objective table for ultra-micro-distance photography comprises a fixed seat, an objective table and a rotating ring, a carrier table is fixedly arranged on the fixed seat, a connecting shaft is arranged between the objective table and the fixed seat, the rotating ring is arranged between the objective table and the fixed seat, and the connecting shaft is arranged between the objective table and the fixed seat. The connecting shaft is sleeved with the rotating ring, the inner diameter of the rotating ring is the same as the outer diameter of the connecting shaft, the distance between the objective table and the fixing base is the same as the height of the rotating ring, and mounting arms are arranged on the side face of the periphery of the rotating ring and arranged in the radial direction of the rotating ring. By means of the device, multi-angle continuous shooting can be conducted on the tiny object, and 3D model construction of the tiny object is completed on 3D reverse modeling software. According to the device, the shooting angle of the tiny object can be stably and accurately regulated and controlled, and reverse 3D modeling of the object is achieved by obtaining stacked images of the tiny object at different angles.

The invention discloses an automatic integral reverse modeling method for a curtain wall keel fusing point cloud and a library. The method comprises the following steps: 1) automatically and positively modeling a curtain wall keel drawing into an IFC model; 2) analyzing the IFC model to obtain output data corresponding to each rod piece in the curtain wall keel; (3) discrete processing is conducted on the output data corresponding to each rod piece in the curtain wall keel, and a standard point cloud corresponding to each rod piece in the curtain wall keel is obtained; 4) obtaining scanning point cloud of the curtain wall keel after completion; 5) matching the scanning point cloud of the curtain wall keel to a coordinate system where the standard point cloud is located; (6) scanning point clouds corresponding to all the rod pieces in the curtain wall keel are obtained through a neighbor search algorithm; (7) feature information of each rod piece is extracted from the scanning point cloud corresponding to each rod piece in the curtain wall keel; and 8) carrying out reverse IFC modeling on the curtain wall keel to obtain a reverse IFC model of the curtain wall keel. And (9) carrying out deep design on the curtain wall panel. According to the method, the whole process is automatic, and reverse modeling can be completed under the condition of data missing.

The invention relates to the technical field of building construction, and provides a curtain wall construction method capable of accurately controlling curtain wall quality and hanging plate seams, aiming at solving the problems that in curtain wall construction in the prior art, hanging plate splicing and positioning are not accurate, abutted seams are difficult to adjust, and slab staggering is formed between hanging plates and abutted seam widths are inconsistent due to the fact that the curtain wall construction is easily influenced by construction disturbance and later settlement. Comprising the steps that S1, a curtain wall hanging plate drawing is deepened; s2, manufacturing a curtain wall hanging plate and a shaped metal sheet; s3, curtain wall embedded plates are installed; s4, curtain wall keels are installed; s5, performing static three-dimensional scanning on the curtain wall keel to construct a curtain wall keel three-dimensional point cloud model; s6, reverse modeling is conducted on the curtain wall keel three-dimensional point cloud model in combination with a curtain wall design drawing, and a curtain wall reverse model is established; s7, the curtain wall reverse model is compared with the curtain wall BIM design model, and the curtain wall BIM design model is adjusted; s8, extracting geometrical characteristics of the curtain wall hanging plate of the curtain wall reverse model; s9, curtain wall hanging plates are installed; and S10, the shaped metal sheets are installed.

The application provides a vehicle path tracking control method and device, electronic equipment and storage medium, comprising: according to the control input type accepted by the vehicle execution mechanism, the reverse modeling of the vehicle lateral movement process under manual operation is carried out, and the control model of the vehicle path tracking is obtained; the current state information and the expected state information of the target vehicle are determined; the current state information and the expected state information are input into the control model, the output parameters of the control model are obtained, and the output parameters are used as the control input of the vehicle execution mechanism to control the path tracking of the target vehicle. In this way, by reverse modeling the vehicle lateral movement process under manual operation, a control model including at least three series control loops is established, so that the control model is more detailed and conforms to the vehicle movement characteristics, which is helpful for the setting of model parameters, and further improves the performance of path tracking control.

The application relates to the technical field of computer-aided architectural design, and discloses an outdoor windproof ceiling BIM parameterized reverse modeling method and system, which comprises the following steps: collecting point cloud data of a ceiling; calculating the local degeneration degree of each point in the point cloud data; dividing point cloud clustering clusters and calculating the average local degeneration degree of the point cloud clustering clusters; calculating the adaptive repair weight of a point according to the distance between the point and the boundary of the point cloud clustering cluster in the point cloud data and the local degeneration degree of the point; determining the adaptive bandwidth of the point according to the adaptive repair weight of the point in the point cloud data; performing point cloud repair on the point cloud data according to the adaptive bandwidth of all points in the point cloud data, obtaining complete repair point cloud data, and completing outdoor windproof ceiling BIM parameterized reverse modeling according to the complete repair point cloud data. The application can improve the quality of point cloud data and accurately model the ceiling BIM parameterization.

The invention discloses a large-curvature blade design optimization method based on rebound reversible deformation calculation, and relates to the technical field of stamping processing, and the method comprises the following steps: carrying out three-dimensional scanning and reverse modeling on a large-distortion stamping blade sample to obtain actual stamping rebound data, and carrying out statistical analysis on the actual stamping rebound data; on the basis of actual production data, a blade reversible deformation design rule and an empirical formula are obtained, a blade design parameter correction value is calculated, automatic blade modeling is achieved through three-dimensional modeling software, and the blanking, stamping and springback processes are simulated in combination with finite element simulation software; and in combination with process automation optimization software, the plate size for blade stamping is obtained through calculation, and the stamping process is optimized. According to the method, blade reversible deformation design data and a stamping optimization process are obtained through process automation software, a stamping displacement, stress and strain cloud picture and a springback displacement cloud picture can be generated, the blade stamping plate size can be obtained, design reference and guidance are provided for accurate stamping forming of the blade, and the production efficiency is improved.

This invention discloses a method for uniformly adjusting the axial air pressure distribution of a vacuum adsorption roller. First, a digital model of the vacuum adsorption roller is accurately constructed using reverse modeling technology combined with 3D modeling software. Then, this model is imported into CFD mesh generation software for fluid domain extraction and mesh division. After refining the mesh in key areas, environmental parameters are configured in computational fluid dynamics software. Next, the boundary conditions of the inlet and outlet under actual working conditions are set. The internal air pressure distribution is obtained through numerical simulation calculation. The simulation results are analyzed to identify the air pressure distribution areas. Based on the analysis results, the area and layout of the air inlets in each axial region are adjusted to balance the air intake and ensure uniform air pressure distribution. Finally, the effectiveness of the adjustment scheme is verified in an actual production environment, and the size and layout of the air inlets are optimized based on measured data until the predetermined air pressure uniformity standard is achieved. This method achieves precise control and optimization of the axial air pressure distribution of the vacuum adsorption roller.