2093 results about "Computer-aided" patented technology

The invention relates to the technical field of computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

The invention discloses a multi-agent computer-aided engineering and a computer-aided design method. CAE simulation and design optimization for the design of a complex product are high in threshold and difficulty. According to the method, autonomous planning and automatic execution of CAE simulation and CAD design optimization tasks are realized by using a multi-agent technology based on a large language model. A user interacts with the multi-agent system in the modes of text input, voice, a graphical user interface and the like, the multi-agent system autonomously plans workflow steps according to the requirements of the user, all agents cooperatively execute the working steps in a labor division mode, CAE and CAD software is called to execute simulation and design change, CAE and CAD result data are read and analyzed, the task completion condition is evaluated, and the task completion condition is evaluated. And the task can be re-planned if necessary. The system can automatically process error information, and can notify a user to process when user intervention is needed. The performance of the system can be improved through fine tuning of a large language model and retrieval enhancement generation technology.

The invention relates to a method for checking spacing in a large-scale integrated circuit layout, which belongs to the technical field of integrated circuit computer aided design, and comprises the following steps: S1, dividing an integrated circuit layout to be processed into a plurality of local layouts according to a preset division rule, including an original local layout and an interference local layout; outwards expanding the edges, determined by a preset division rule, of the interference local layouts to obtain expanded local layouts; s2, checking whether the edges of all the functional parts in all the local layouts meet the constraint condition upper limit corresponding to the selected preset checking rule one by one according to the currently selected preset checking rule so as to screen out the edges meeting the potential conditions; s3, forming associated edge pairs, and screening the associated edge pairs through a filter to obtain associated edge pairs meeting filtering conditions; and S4, carrying out secondary cutting on the associated edge pairs according to the cutting ranges of the associated edge pairs meeting the filtering conditions.

The invention discloses a digital steel structure embedded part accurate positioning system, and belongs to the technical field of computer aided design and digital twinning, and the system comprises a data acquisition module which is used for obtaining a correction vector of a digital twinning model, the currently measured local positioning precision and the current global attitude consistency deviation; the model entropy calculation module is used for calculating a model authority entropy representing the accumulated deviation degree of the digital twin model based on the correction vector and a preset reference correction scale; the model authority calculation module is used for determining the model authority of the digital twin model according to the model authority entropy and a preset attenuation sensitivity coefficient; and the correction decision module is used for generating a correction acceptance probability in combination with the model authority and the local positioning precision, and performing probabilistic screening on the execution of the correction vector based on the correction acceptance probability, thereby providing a solid quantitative basis for subsequent judgment of the health condition of the model.

The invention discloses a new energy automobile body frame lightweight design method based on digital twinning, and relates to the technical field of new energy automobile body structure design and computer-aided engineering simulation, and the method comprises the following steps: S1, building a digital twinning body connected with a physical automobile body sensing system; s2, a dynamic precision topology network module is used for dynamically dividing a simulation precision area of the vehicle body frame according to the mechanical energy transmission path and the real-time sensing data. According to the new energy automobile body frame lightweight design method based on digital twinning, intelligent allocation of computing resources is realized through a dynamic precision topology network, and the computing burden of multi-physics coupling simulation is effectively reduced while precise prediction of a key structure is ensured; the sharp contradiction among the model precision, the real-time response and the optimization efficiency is effectively solved, and effective closed-loop iteration of the lightweight design is achieved under the complex constraint condition.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention relates to the technical field of fuel cell system anomaly detection, in particular to a fuel cell system time series data anomaly detection method based on residual analysis, and the method comprises the specific implementation steps: obtaining multi-source design simulation data of a system, and constructing a multi-source design simulation data tensor; constructing a computer aided design model, performing behavior simulation on the simulation data tensor, and outputting a simulation design behavior value; the simulation data is used as a simulation observation value to be compared with a simulation design behavior value, a multi-dimensional residual sequence is obtained through calculation, and online self-adaptive updating is conducted on the design model; establishing a physical reachability matrix, and adjusting a dynamic threshold value based on the matrix and a real-time simulation working condition; performing weighted fusion on the multi-dimensional residual sequence to generate a collaborative design constraint index, and comparing the collaborative design constraint index with the dynamic threshold to generate a computer aided design verification result; and inputting the design verification result into a preset residual-control mapping rule, and outputting a design parameter optimization instruction to optimize the design parameters.

The invention relates to the technical field of computer aided design and intelligent optimization, in particular to a sponge park multi-objective collaborative optimization design method based on an LID technology, and the method comprises the steps: building an LID facility parameterized database through collecting the multi-source data of geographic space, hydro meteorology and the like; a multi-objective collaborative optimization model covering hydrological, ecological, economic and social benefits is established, a mixed intelligent optimization algorithm composed of an improved NSGA-II algorithm and an enhanced particle swarm optimization algorithm is used for solving, and a Pareto optimal solution set is generated; screening a comprehensive optimal scheme by means of an SWMM and GIS coupling technology and an improved TOPSIS method; an Internet of Things monitoring network and a digital twinborn system are deployed, and self-adaptive dynamic optimization of a design scheme is realized according to triggering conditions such as runoff coefficient deviation and vegetation coverage rate change. The method breaks through the limitation of a traditional single target, remarkably improves the comprehensive benefits of the sponge park through multi-target cooperation, intelligent algorithm optimization and dynamic adjustment, and guarantees the long-term stable operation of the sponge park in a complex environment.

The invention discloses an unsteady flow field dimension reduction and prediction method fusing physical modeling and deep learning, and belongs to the technical field of computer-aided fluid mechanics analysis. According to the method, firstly, a modal coefficient reflecting global dynamics is extracted from an unsteady flow field by using a DMD, and meanwhile, low-dimensional feature representation of a potential space is learned from a flow field snapshot through CVAE; the two types of features have complementarity in physical and statistical meanings, and the complex dynamic evolution law of the unsteady flow field is more effectively represented through the low-dimensional features constructed in a combined mode. On the basis, an LSTM model is used for carrying out time sequence modeling on the joint features, and high-precision prediction of future evolution of the flow field is achieved. The hybrid modeling method provided by the invention improves the dimensionality reduction efficiency and prediction precision of a high-dimensional nonlinear unsteady flow field while keeping physical consistency, and is suitable for intelligent simulation and rapid prediction tasks in a complex flow scene.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention relates to the technical field of computer vision, computer aided design and artificial intelligence, and discloses a geometric feature recognition method based on a large language model, which comprises the steps of three-dimensional model natural linguistization, large language model feature reasoning, incremental learning and optimization, and feature verification and output. According to the geometric feature recognition method based on the large language model, three-dimensional geometric data are converted into structured text description, and a joint representation space of'geometric parameter-topological relation 'is constructed, so that the large language model can better understand the meaning of geometric features and the structure of parts; the method comprises the following steps: analyzing topological association among geometric features by utilizing long context understanding ability of a large language model, capturing dependency among the features through an attention mechanism, realizing hierarchical recognition of the features in a complex assembly, dynamically updating a geometric feature knowledge base of the large language model by continuously learning new geometric cases and engineering specifications, and realizing hierarchical recognition of the features in the complex assembly. Adaptive recognition of novel features is supported, and the model does not need to be retrained.

A system and method are provided for aligning a tool with a targeted axis in tissue to perform a medical procedure. A medical plan is registered to the location of the tissue using a computer-assisted medical system, where the medical plan include a planned position for the targeted axis based on pre-procedure data. The tool is aligned with the planned position for the targeted axis using a computer- assisted medical system. The computer-assisted medical system includes a hand- held device having a handle and a working portion adjustable relative to the handle so as to orient the tool. A computing system is also provided comprising a tracking system and a control system for registering the medical plan to the location of the tissue, tracking the hand-held device relative to the tissue and the medical plan, and adjusting the working portion of the hand-held device relative to its handle.

The invention relates to the technical field of data processing, in particular to a computer-aided air conditioner load prediction model simulation construction method, which comprises the following steps of: acquiring data of each environmental parameter in a building at all moments in each period in real time to form a monitoring sequence; the method comprises the following steps: performing analogue simulation on energy consumption of an air conditioner to obtain load data of an air conditioner load at all moments in each period, and forming a load sequence; acquiring a lagging time length; extracting a time delay subsequence of each environmental parameter in each period; the trend synchronization degree between each environment parameter and the air conditioner load in each period is determined; determining a coupling coefficient of any two environmental parameters in each period; determining a reference evaluation value between the current period and each of the rest periods; obtaining each reference period; and constructing a load prediction model. According to the method, prediction deviation caused by neglecting interaction influence and lag influence among various environmental parameters can be avoided, and the prediction accuracy of the air conditioner load is improved.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the technical field of computer-aided manufacturing, and provides a staged processing real-time cutting simulation method and system.The method comprises the steps that S1, initialization modeling is conducted, specifically, a blank voxel grid model, a tool body voxel grid and a triangular grid are built; s2, performing a first simulation stage of low-precision real-time cutting dominated by voxel grids, judging a space overlapping relationship between the tool body voxel grids and the blank voxel grids in real time so as to determine a voxel region to be removed, and deleting blank voxels in the overlapping region through Boolean operation; s3, in the execution process of the first simulation stage, monitoring a preset multi-dimensional switching trigger condition in real time, and when any condition or a combination of multiple conditions is met, entering a second simulation stage; and S4, a second simulation stage of high-precision real-time cutting with the triangular network as the dominant is carried out, the voxel grid intermediate workpiece is converted into a transition triangular grid, and high-precision cutting calculation is carried out based on the triangular grid. And the efficiency-precision contradiction is broken through through a staged strategy.

The invention discloses a 3D art model intelligent generation system and method based on multi-source data fusion, and relates to the technical field of computer aided design and graph generation, and the system comprises a multi-source data input module which is used for receiving a conceptual design drawing, text description and a reference 3D model; the feature extraction module comprises an image feature extraction unit used for extracting a style feature vector and a structure contour vector of the conceptual design drawing; and the text semantic analysis unit is used for outputting a key attribute tag vector described by the text. According to the 3D art model intelligent generation system and method provided by the invention, through multi-source data input including the concept design drawing, the text description and the reference 3D model, the design intention is comprehensively fused, the limitation of a single data source generation model is solved, and the dynamic weight distribution strategy and the closed-loop optimization mechanism of the 3D art model intelligent generation system and method are high in practicability. The geometric shape and style characteristics of the generated model are highly consistent with the design intention, and the workload of manual later correction is reduced.

The invention discloses a Solidworks model-based machining program generation method and system, and belongs to the technical field of computer-aided manufacturing, and the method comprises the steps: S1, analyzing the modeling features of each part in a Solidworks model, carrying out the matching analysis of the modeling feature sketch contour and sketch plane normal vector with a feature library, obtaining a machining feature category, and carrying out the recognition of the machining feature category; performing feature grouping according to the processing feature category and the processing direction; s2, identifying and extracting the part size and tolerance marked in the sketch; s3, analyzing a part machining process based on the machining feature category, the part size and the tolerance; s4, the blank type, machining allowance distribution, machining steps and a clamping mode are determined; s5, the three-dimensional space coordinates of the machining contour are converted into machine tool machining coordinates, and machining information is sequenced; s6, the machining path of the part machining technology is calculated; and S7, according to the machining path, combining with an instruction supported by a specific machine tool, performing post-processing to generate a G code.

The invention relates to the technical field of computer aided design, in particular to a BIM-based garden landscape optimization method and system, and the method comprises the following steps: extracting a spatial data model in a BIM based on the spatial feature data of garden landscape elements, carrying out the sight tracking of garden viewing nodes, capturing the visual data of different angles, and carrying out the calculation of the visual data. And analyzing the visual area range and the visual focus attraction to obtain the garden landscape sight line access coverage rate. According to the invention, through landscape data acquisition, sight tracking and illumination analysis, improvement of visual accessibility identification, quantitative evaluation of footpath environment factors, improvement of path experience judgment precision, path structure combination intersection relation and substitutability optimization, improvement of connectivity and viewing continuity, and facility layout fusion interference identification and function coordination; the space partition reasonability is improved, path and element matching is adjusted, the space response capability is enhanced, conversion from static analysis to dynamic scheduling is achieved, and the layout adaptability and the touring integrating degree are improved.

The invention relates to the technical field of computer-aided drug research and development, and provides an antibacterial peptide function recognition optimization method and system based on deep learning and LLM. Systematic innovation is performed in multiple links such as feature extraction, data generation and reasoning verification by introducing a fusion mechanism of a large language model and a deep learning model; and the accuracy and robustness of antibacterial peptide function prediction are obviously improved. On one hand, a strict feature extraction process and a structured cue word template are constructed, sequence information and physicochemical property indexes of the antibacterial peptide and a prediction result of a deep learning model can be effectively integrated, and the prediction result is reasonably corrected and optimized through the reasoning ability and knowledge verification mechanism of a large language model. The misjudgment risk of a traditional deep learning method under the condition of insufficient samples or incomplete features is effectively reduced, and the prediction accuracy and stability are remarkably improved.

The invention belongs to the technical field of computer aided design, and provides a spinning equipment design assisting method and system based on model collaboration. The method comprises the steps that a collaborative design system is constructed, a large language model receives design requirements of a user, performs task decomposition and calls corresponding professional agent models to execute design subtasks, and the professional agent models feed simulation calculation results back to a closed-loop feedback engine; the closed-loop feedback engine analyzes feedback results of the professional agent models and generates attention prompt signals pointing to potential design conflicts or optimization bottlenecks on the basis of interdisciplinary parameter coupling relations; and the large language model performs multidisciplinary coupling analysis by using the attention prompt signal, dynamically adjusts task planning and design parameters by focusing on key parameters and key disciplines pointed by the attention prompt signal, and drives related professional agent models to perform directional iterative optimization. According to the method, the blind iteration problem can be effectively avoided, and the convergence speed of the design and the scheme quality are improved in a high-quality mode.

The invention belongs to the technical field of computer-aided project management, and discloses an electric power engineering design project management system and method, and the system comprises a data integration and processing module, a multilayer dynamic knowledge graph data storage library, a graph-based automatic compliance check engine, and a graph neural network-based predictive risk engine. And an active design change impact analysis module. According to the method, a unified and intelligent project data core is constructed, heterogeneous data sources such as design files and law and regulation texts are integrated in a multi-layer dynamic knowledge graph, automatic compliance review is realized by utilizing graph query, data-driven risk prediction is realized by utilizing a graph neural network GNN, and the risk prediction efficiency is improved. And the linkage influence of design change can be simulated and quantitatively evaluated. According to the method, the problems of data islanding, passive compliance examination, subjective risk management, difficulty in change analysis and the like in the prior art are solved, and the management efficiency, accuracy and initiative of the electric power engineering design project can be remarkably improved.

The invention discloses a special-shaped pipe forming compensation method and system based on digital twinning, and belongs to the technical field of computer-aided manufacturing and intelligent control. The method comprises the steps that multi-source sensing data in the special-shaped pipe forming process is obtained, and time-space fusion processing is executed; generating a time-space aligned multi-source sensing data set, carrying out dynamic calibration on the parameterized virtual digital twin, generating a calibrated virtual digital twin, carrying out simulation calculation on the forming process of the special pipe in a future time window, outputting predicted deviation data, and combining with a preset compensation strategy to generate a compensation instruction through logical judgment and calculation; and obtaining an adjustment result after the compensation instruction is applied, and updating the compensation strategy by using a reinforcement learning algorithm. According to the method, prospective simulation prediction is carried out by constructing the dynamically calibrated virtual digital twins, closed-loop optimization is carried out on a compensation strategy by utilizing a reinforcement learning algorithm, and active, high-precision and self-adaptive compensation control on the special pipe forming process can be realized.

The invention provides a finite element analysis method for a competition-level site cross-country motorcycle frame, and belongs to the technical field of crossing of computer-aided engineering and vehicle structure design. The method comprises the following steps: performing parametric modeling on a frame main bearing structure based on CATIA software, and simplifying non-functional characteristics; importing the model into ANSYS Workbench, generating a high-precision finite element model by adopting a grid division strategy of mixing tetrahedron and hexahedron units, and considering the calculation efficiency and precision; defining material characteristics; a limit load working condition is simulated through statics analysis, a displacement cloud picture and a stress cloud picture are combined to be compared with the yield strength of a material, and topological optimization is conducted on a weak area; meanwhile, the inherent frequency of the frame is extracted through modal analysis, and the resonance risk is avoided. According to the method, statics analysis and modal analysis are carried out on the frame through cross-platform co-simulation, the structural reliability is evaluated, and the method is suitable for efficient development and performance optimization of competition-level cross-country motorcycle frames.

The embodiment of the invention is suitable for the technical field of computer-aided medical treatment and image processing, and provides an automatic planning device and equipment for a pedicle screw and a computer program product. Extracting vertebral body outer layer point cloud data of a to-be-planned single vertebral body from the spine image data; the prediction model is used for processing the centrum outer layer point cloud data and outputting prediction coordinates, and the prediction coordinates comprise entry point coordinates and exit point coordinates; and the planning unit is used for planning the placement position of the pedicle screw based on the entry point coordinate and the exit point coordinate. By adopting the device, the planned position of the pedicle screw can be quickly and accurately predicted by using the deep learning model based on the point cloud data of the outer layer of the vertebral body, the data volume needing to be processed in the planning process is reduced, and the planning efficiency is improved.

The invention discloses a construction product style migration method based on a diffusion model and structure control, and belongs to the field of computer-aided industrial design. Obtaining a structure reference STEP model and a target style image, and respectively extracting feature token sequences of the structure and the style; splicing the structure and style feature token sequence as a mixed condition vector of the conditional diffusion model; when the model diffuses in a submerged space, decoupled double-path attention layers are used for calculating cross attention output of structure and style features respectively, time-varying weights are added to serve as cross attention output of hybrid conditions for decoupling coding, and a 2D rendering graph conforming to a target structure and style is generated through gradual de-noising. The conditional diffusion model is run a plurality of times to generate a batch of candidate 2D renders. And screening out a qualified 2D rendering graph based on NURBS curved surface reconstruction. According to the invention, a structure-style dual-condition control mechanism is adopted to realize accurate balance between high-fidelity style migration and engineering constraint.

The invention discloses an aviation three-dimensional aided design integration method and system based on a large language model. The method comprises the steps of obtaining multi-modal design demand data submitted by a user; analyzing the multi-modal design demand data submitted by the user; and calling a large language model to generate a corresponding modeling command according to the analyzed multi-modal design demand data, calling a computer aided design system by utilizing the modeling command to carry out modeling design, and optimizing the large language model at the same time. The system comprises a user interaction module for receiving multi-modal design demand data input of a user; the data analysis module is used for analyzing the multi-modal design demand data and extracting a design demand; the modeling instruction generation module is used for calling a large language model to generate a modeling instruction according to a design requirement; the modeling execution module is used for calling a computer aided design system API to complete modeling by utilizing the modeling instruction; and the model optimization module is used for feeding back the model data modified by the user to the large language model for optimization.

A computer-aided system and associated method for automated building design includes receiving input parameters and generating a three-dimensional parametric computer model of a building frame and populating the three-dimensional computer building model with generic and manufacturer-specific products by accessing internal and external data sources. The populated computer model undergoes real-time simulation to analyze regulatory compliance, verify engineering requirements including load calculations, and evaluate cost considerations. Based on simulation results, the method generates output documents including requests for proposals (RFP documents) for contractors, requests for quotations (RFQs) for vendors, and regulatory compliance documentation. These documents are transmitted to respective stakeholders through a secure data exchange protocol. The method further includes receiving responses to the generated RFP documents / RFQs.

The invention relates to the technical field of computer aided design, in particular to a steel plate pitting corrosion simulation method and system based on a cellular automaton model.The method comprises the steps that all slope curve segments corresponding to all corrosion pit morphology curves are obtained, and the directional variation index of each depth position is determined according to the depth position of the slope curve segments; acquiring a plurality of slope areas in the corrosion pit under different corrosion time with different solution concentrations to analyze the abnormal corrosion condition, and determining the local corrosion progressive coefficient of each depth position by combining the expansion coefficient; obtaining the number of intersections between the depth positions of each type of slopes and each type of local erosion progressive coefficients, and generating a dynamic rule of evolution of the cells to the corresponding type of slopes in combination with a directional variation index; on the basis of a dynamic rule, the cellular automaton model with the optimal goodness of fit is obtained by training the LSTM network, the problem of directional evolution under a finite element rule is avoided, and powerful help is provided for researching the real pitting process of the steel plate.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention relates to the technical field of computer-aided engineering analysis, and discloses a vibrating screen finite element analysis method and system.The vibrating screen finite element analysis method comprises the steps that three-dimensional structure parameters of a vibrating screen are obtained, a global finite element structure model is established, and material parameters and boundary conditions are set; constructing a disturbance load input set, performing multi-working-condition simulation analysis on the global finite element structure model based on the disturbance load set, obtaining stress distribution results, and identifying a high-risk region; extracting a local structure corresponding to the high-risk area, constructing a local finite element structure model, and performing finite element simulation; and based on a local finite element simulation result, correcting response parameters of a high-risk region in the global finite element structure model, and updating stress distribution of a global structure. The closed-loop correction and consistency enhancement of the structure response are realized, and the problems of insufficient global precision and difficulty in local information feedback in the existing method are solved.