54 results about "Modelling methods" patented technology

The invention discloses a concrete mesoscopic modeling method based on the calculus principle, and relates to the field of concrete modelling, and the technical scheme comprises the following steps: S1, initializing geometric model parameters; s2, generating aggregate information; s3, interference condition judgment: carrying out interference condition judgment on the generated aggregate; s4, boundary condition judgment: checking whether the aggregate is completely located in the putting domain or not; s5, storing and updating aggregate information; and S6, judging whether the current aggregate total volume reaches a preset volume or not. The method has the beneficial effects that the particle size and position information of the aggregate is randomly generated by introducing the Monte Carlo principle, and random distribution of the aggregate in a putting domain can be realized; the optimal Fuller grading curve proposed on the basis of the calculus principle can greatly reduce the time for generating the model and improve the efficiency of model generation.

The invention relates to the technical field of electric power work teaching, in particular to an electric power work teaching-oriented multi-modal data fusion VR scene automatic modeling method, which can efficiently and accurately extract key semantic information related to electric power work from multi-modal data through a deep learning-driven semantic recognition and label generation method, so as to improve the teaching efficiency of the electric power work. Multi-modal fusion is carried out by introducing a graph neural network structure, the problems of data splitting, semantic missing, scene stiffness and the like in a traditional modeling mode are effectively solved, comprehensive transition from low-level perception to high-level understanding is achieved, a generated three-dimensional VR scene model is closer to a real electric power operation environment, and teaching efficiency is improved by automatically generating and embedding teaching scripts. According to the system, the virtual teaching content is highly consistent with the actual operation process, the problems of subjectivity and inconsistency in the traditional manual script input process are effectively avoided, and the visualization effect and the real-time response capability of a teaching scene are effectively improved.

The invention provides a life cycle evaluation automatic modeling method based on a large language model, and relates to the technical field of life cycle evaluation, and the method comprises the steps: extracting the multi-modal information of texts, tables, images and the like from the multi-source data of academic literatures, reports and the like, constructing an instruction data set in combination with LCA process knowledge, carrying out the instruction fine tuning of a base large model, and carrying out the automatic modeling of the life cycle evaluation. And obtaining the LCA field large language model. And generating a standardized prompt by utilizing a prompt word optimizer, driving a model to generate a product LCA model framework, filling list data, and perfecting process data in combination with semantic search and a correction mechanism. And intelligent connection recommendation among unit processes is realized by calculating the semantic similarity of input and output streams. And finally, the verified structured data is output to LCA modeling software, an automatic modeling process is completed, and the modeling efficiency and normalization are improved.

The invention discloses a fluid-solid-sound coupling calculation and noise reduction method for a bent pipe, and belongs to the technical field of pipeline testing. According to the method, the three-dimensional physical model of the bent pipe is constructed, the bent pipe fluid-solid coupling model is established, and the dynamic correlation between the flowing characteristics of fluid in the bent pipe and the response of a solid structure is accurately reflected; the method can capture flow field disturbance caused by inertia, centrifugal force and geometric changes of the elbow pipe in the flowing process, quantitatively describes a dynamic load change rule applied by fluid to the pipe wall, effectively improves understanding of complex elbow pipe flow field behaviors, extracts wall surface pressure distribution data, generates fluid disturbance data, and improves the flow field performance of the elbow pipe. According to the method, the distribution characteristics of noise sources generated by flow-induced vibration can be accurately predicted, the problem that a traditional single-field modeling method cannot fully describe actual working conditions is solved on the whole, the physical authenticity and prediction precision of simulation are greatly improved, and the problems of high cost and low efficiency caused by a traditional trial and error method are effectively avoided.

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

The invention relates to the field of three-dimensional modeling and artificial intelligence control systems, in particular to a large language model driving type FMT automatic modeling method supporting an MCP protocol. According to the method, a Python interpreter is embedded into FMT modeling software, a core modeling operation function is registered as a Python callable script, a remote instruction format defined by an MCP (Model Control Protocol) protocol is matched, and a user describes a modeling intention through a natural language; then decomposing a natural language input by a user according to intentions by using a large language model, generating a complete FMT modeling program, and automatically manipulating the FMT to complete modeling operation; and finally, a full-chain closed loop of natural language-script instruction-software operation behavior-real-time feedback is realized, a large language model is supported to directly control an actual software modeling process, and the automatic modeling capability is greatly improved.

The invention discloses a multi-dimensional writing evaluation modeling method based on course standard isomorphism, and relates to natural language processing, education evaluation and text discourse analysis technologies. According to the method, a course standard writing target system is taken as a constraint, a course standard capability structure is analyzed and reconstructed, a writing capability dimension set consistent with the course standard capability structure is constructed, and a structured modeling framework of a dimension-index-evidence chain is further established. In the method, unified measurement of writing features is realized by adopting an index quantization function, and aggregation of dimension scores is completed in combination with a learnable weight matrix; and meanwhile, corresponding evidence fragments and evidence chains are generated, so that traceability of score sources is realized. Compared with a traditional automatic composition scoring method which only gives a single score or a coarse-grained dimension score, the method can provide a multi-dimensional, explainable and calibratable writing ability evaluation result on the basis of a course standard, and the standardization, the consistency and the teaching availability of the evaluation process are remarkably improved.

The invention discloses a composite material defect modeling method based on physical modeling generation and meta-learning migration, which comprises the following steps: S10, starting from a forming mechanism of a fatigue crack of a composite material, establishing a morphological function of a crack morphological simulation model based on a mechanical principle for simulating a spatial evolution process of a defect; s20, embedding the morphological function as a regular term into a generator loss function of a generative adversarial network, and generating a high-simulation pseudo-defect image; s30, introducing a transfer learning mechanism, constructing a model-irrelevant meta learning training strategy, and realizing rapid learning of the model on a new task through a two-layer nested optimization strategy; s40, introducing a domain adaptive residual module in the model training process to enhance the feature alignment capability of a cross-batch image domain; and S50, training the model by using a real sample and a synthetic defect sample in a mixed manner.

The invention discloses a tactile common sense reasoning-oriented tactile-language large model modeling method. The method comprises the steps of S1, preparing a first data set and a second data set; s2, constructing a tactile-language large model, wherein the tactile-language large model comprises a tactile encoder, a tactile-language adapter and a large language model LLM; s3, performing two-stage training on the tactile-language large model, and training the tactile-language large model by adopting the first data set in a first training stage; in a second training stage, training the tactile-language large model after the first training stage by adopting a second data set to obtain a trained tactile-language large model; and S4, in a test stage, inputting the second data set into the trained tactile-language large model to obtain text description. According to the tactile-language large model modeling method, the parameter coupling risk is reduced, robust fusion of efficient convergence and cross-modal representation is realized, and the training stability is remarkably improved.

The invention relates to a BIM data large model-based Revit modeling method and system, and the method comprises the steps: building a modeling model according to a multi-modal training data set, and the modeling model comprises a feature extraction module, a rule base module, a topological relation module and a rule mapping module. And in response to instruction information of a modeling demand input by a user, extracting structured feature parameters through a feature extraction module. And matching the structured feature parameters with the building rules in the rule base module to obtain the building rules meeting matching conditions. And based on the building rule, obtaining a component topological relation through a topological relation module. And based on the component topological relation, generating a building model meeting modeling requirements through a rule mapping module. The problem that in the prior art, a Revit modeling method based on a BIM data large model is low in automatic modeling efficiency is solved.

The invention discloses a crowd portrait interest map enhanced modeling method based on deep learning. The method comprises the following steps: S1, generating a crowd portrait interest map snapshot sequence; s2, obtaining a performance evaluation result set; s3, selecting optimal pooling hyper-parameter configuration according to a fitness evaluation result; s4, outputting a time window hierarchical structure vector set; s5, splicing the time window hierarchical structure vector set according to a graph snapshot sequence time sequence to obtain an interest fusion representation vector, and constructing an offline teacher model; s6, performing knowledge distillation training on the lightweight student model with the simplified structure, deploying the lightweight student model in an online real-time recommendation system, and outputting a reasoning result in a real-time traffic scene; s7, when it is detected that the key performance index is lower than a preset threshold value or the user interest distribution changes larger than the preset threshold value, the step S2 to the step S6 are executed again. According to the method, the recognition capability of complex behavior modes of cold start users and interest transfer users is effectively improved.

The present application provides a modeling client and a modeling method based on flow relationships. The modeling client is used to describe business systems in various fields based on a modeling language, including multiple types of classes and flows; each type is used to describe objects with the same attributes, and flows are used to be configured between different types of classes to decouple the connection relationships between different types of classes so as to perform parallel control of the processes between different types of classes. The present invention isolates tasks or processes that were originally closely connected through flows. For example, producers and consumers are no longer directly connected, but information is transmitted through a flow, so that each part can work independently, reducing the complexity of the entire system and significantly improving the modeling efficiency and expression ability of multi-input and multi-output relationship logic. At the same time, due to the simplification of the modeling logic, errors caused by unclear expression are reduced, ensuring the consistency and accuracy of the model logic.

The invention discloses a gas-solid interaction scattering nuclear model modeling method based on a corrected characteristic temperature, and the method comprises the steps: determining the average incident energy according to the macroscopic temperature and macroscopic speed of incident gas; calculating tangential and normal energy adaptation coefficients and a total energy adaptation coefficient according to given tangential and normal momentum adaptation coefficients; constructing a gas reflection temperature based on the average incident energy, the wall surface temperature and the total energy adaptation coefficient; calculating tangential correction characteristic temperature according to the reflection temperature and the tangential momentum adaptation coefficient; calculating a normal correction characteristic temperature according to the reflection temperature and the normal momentum adaptation coefficient; and respectively replacing the wall surface temperatures of the tangential and normal scattering nuclei of the original CLL model with the tangential and normal corrected characteristic temperatures to obtain a gas-solid interaction scattering nucleus model based on the corrected characteristic temperatures. According to the method, the characteristic temperature of the original CLL model is corrected according to the incoming flow parameters on the premise that the number of adjustable parameters is not increased, and the simulation precision of the gas scattering behavior is improved.

The application discloses a risk control modeling method and device based on a large model agent and equipment, comprising: receiving a natural language modeling instruction input by a user, and inputting the natural language modeling instruction into a fine-tuned intention recognition large model for analysis, and outputting a corresponding structured function call instruction sequence; inputting the structured function call instruction sequence into a risk control modeling agent to generate a corresponding task execution plan; selecting an execution path according to the task type of each function call instruction in the task execution plan through the risk control modeling agent; calling a corresponding remote tool service or a local function function to execute the current task according to the execution path, and returning an execution result; receiving and integrating the execution result of each function call instruction through the risk control modeling agent, and determining and executing subsequent operations according to the obtained integrated result, wherein the subsequent operations include continuing to execute the next function call instruction, initiating an interactive inquiry to the user or outputting a final risk control modeling result.

The present invention discloses a boiler multi-region CFD-AI modeling method and system based on federated learning, which belongs to the field of boiler monitoring technology. The specific steps of the modeling method are as follows: S1, simulating the heat transfer and fluid behavior of the boiler, establishing a multi-region CFD grid division, and collecting and preprocessing the dynamic operation data of each region from the actual operation of the boiler; S2, extracting the features of the preprocessed dynamic operation data, constructing a boiler multi-region CFD-AI model based on the extracted features, and globally optimizing the boiler multi-region CFD-AI model; S3, dynamically adjusting the structure and parameters of the globally optimized boiler multi-region CFD-AI model, and enhancing the key area decision-making of the boiler multi-region CFD-AI model; the present invention can improve the efficiency of model training, save a lot of communication resources, reduce the risk of privacy leakage, and promote the cross-region adaptability of the model, significantly improve the performance of the model under complex working conditions, can effectively handle multi-objective optimization problems, and can more accurately predict and control the performance of the boiler in different regions and working conditions.

The invention discloses a modeling method for component credibility in a station house pipe gallery information model, which comprises the following steps of: generating error mode characteristics according to a construction process section of a boundary component, analyzing a conflict mode among pre-stored multi-source data by utilizing the error mode characteristics, and calculating single credibility of the boundary component; constructing a process block-structure block double-layer graph comprising a process block layer and a structure block layer, and respectively associating the boundary components to process block nodes and structure block nodes in the process block-structure block double-layer graph; and configuring an anisotropic propagation weight according to the error mode characteristics, executing spatial propagation of the credibility of the single body on the process block-structure block double-layer graph, and generating a spatial credibility field. According to the method, the construction logic and the structural boundary are explicitly coded, so that the problems that errors cross physical boundaries and are diffused and multi-source data conflicts are difficult to explain in a traditional method are solved, and accurate evaluation on the credibility of the component in the complex boundary area is realized.

This application discloses a modeling method based on a dual attention mechanism. It takes a four-dimensional tensor as input data; extracts the spatiotemporal features of the input data through a deep convolutional long short-term memory network, applies a ConvLSTM2D layer to each feature channel of the input, and splices them in the feature dimension; integrates the time-distributed spatial attention and feature attention into a deep learning framework, and expands the spatial features of each time step into a one-dimensional vector through a time-distributed Flatten layer; applies a global average pooling layer to the one-dimensional vector to aggregate the features in the time dimension; and predicts the target value through the output layer. By utilizing the dual attention mechanism, the present invention uses an algorithm to model important spatial positions and features that change over time, without the advantage of pre-analyzing data, thereby improving the prediction accuracy, interpretability, and generalization ability in multiple spatiotemporal scenarios.

The invention discloses a modeling method and a modeling system, and relates to the technical field of three-dimensional modeling. The modeling method is based on a multi-degree-of-freedom motion platform. The first movement mechanism, the second movement mechanism and the third movement mechanism of the multi-degree-of-freedom movement platform are controlled to be matched to drive the carrying table to move, so that the camera shooting equipment collects image information of a to-be-modeled target. The multi-degree-of-freedom motion platform can drive the camera equipment to move flexibly, the flexibility of the camera equipment is improved, and the multi-degree-of-freedom motion platform can adapt to diversified scanning paths so as to meet modeling requirements of more application scenes. In addition, the number of the camera devices used in the method is small, and therefore the modeling cost is low.

The present application relates to a kind of socket connection pipeline parameterized modeling system and modeling method, belong to computer aided engineering field.The present application is driven finite element software by Python script, realizes the automatic generation from standard drawing size to complete solution input file.The core steps include: parameter is collected by graphic interface;Based on analytical geometry algorithm, the control point coordinates of complex profile of socket, spigot and T type rubber ring are accurately solved;Automatic model is built and material attribute is given;According to geometric feature, surface domain is automatically cut and anti-lock gradient grid is generated;"shrinkage-expansion-insertion" multi-step analysis sequence based on virtual temperature field is created to solve large interference contact problem;Finally, standard INP file is exported by one key.The present application solves the defects of low efficiency, easy to make mistakes and difficult to standardize of traditional manual modeling, provides full-process automatic solution for large-scale, high-precision simulation of pipe interface.

The invention provides a modeling method, a filler, a modeling device, electronic equipment and a storage medium. The modeling method comprises the following steps: constructing a TPMS basic unit through modeling software, and setting related parameters of the TPMS basic unit; constructing an initial layer based on the TPMS basic unit; stacking the plurality of initial layers to obtain a first filler; respectively setting the aperture of the first filler according to a plurality of preset aperture values to obtain a plurality of second fillers; the plurality of second fillers are in one-to-one correspondence with the plurality of preset aperture values; performing piecewise function fusion on the plurality of second fillers to obtain a target filler; the apertures of the plurality of TPMS structural layers of the target filler are increased from top to bottom.

The invention relates to the technical field of intelligent education, and discloses a multi-modal interactive education content generation method and system. The method comprises the steps of obtaining multi-modal generation data, performing time sequence prediction processing and constructing a time scale model, and determining a modal dynamic value; simulating a resource allocation scene by adopting a probability simulation method based on the modal dynamic value to obtain a cost increasing rule, and adjusting a resource allocation scheme through an optimization algorithm; the time scale model is updated through a feedback cycle mechanism, and the generation time estimation precision is improved; acquiring education content input in real time, judging a resource demand, and if the resource demand exceeds a threshold value, activating the standby resource pool to perform cost control; according to the method, bottleneck points are identified through a clustering analysis method, efficiency improvement indexes are extracted, the multi-modal fusion stability is judged in combination with a sequence modeling method, a resource optimization path is generated, and the resource allocation efficiency and stability of multi-modal education content generation are improved.

The present invention relates to the technical field of human body modeling, and in particular to a cross-domain unified human body modeling method, apparatus, equipment and storage medium. The present invention uses a relative posture similarity space to represent the relative similarity between any two skeleton postures of the human body, and then extracts the relative similarity between the unsampled skeleton posture and the anchor prompt from the relative posture similarity space. Based on the relative similarity, the target posture is screened out from the unsampled skeleton posture, and the anchor prompt set is updated with the target posture to obtain a final set of anchor prompts. Finally, the anchor prompts and soft prompts in the final set of anchor prompts cooperate with each other to complete the reasoning task of the model. Since the soft prompts are based on the parameters generated when the model is trained for each reasoning task, when processing different reasoning tasks, it is only necessary to replace the soft prompts corresponding to the reasoning task to cooperate with the anchor prompts to complete the reasoning task without replacing the model. Therefore, the present invention can achieve unified modeling across tasks.

The invention discloses a parametric modeling method based on three-view adaptive projection, which is suitable for a task of automatically generating a structured modeling instruction from three-dimensional point cloud data and can realize CAD (Computer Aided Design) structure reconstruction with high geometric consistency and semantic interpretability. The method comprises the following steps: firstly, extracting global features by using a point cloud encoder to predict a rotation attitude in an Euler angle form, and performing rotation alignment on a point cloud; then projecting the rotated point clouds to three groups of orthogonal planes respectively, extracting a high-quality projection point set by combining adaptive grid sampling of normal filtering and density sensing, extracting structured two-dimensional features by adopting a hierarchical K-nearest neighbor aggregation mechanism based on a three-view projection result, and further guiding a parameter decoder to generate a sketch and a stretching modeling instruction; the method has the remarkable advantages in the aspects of structure alignment precision, complex geometry adaptability and program editability, and is suitable for application scenes such as reverse design, industrial part modeling, building sketch reconstruction and intelligent auxiliary modeling.

The invention relates to the technical field of steel structure drawing, in particular to a modeling method and device for a frame structure and a storage medium. The method comprises the steps that a modeling environment is created, the modeling environment comprises a plurality of free datum lines, and the free datum lines are staggered to obtain a plurality of modeling nodes; in response to the modeling operation, a frame structure type selected by a user and a plurality of target modeling nodes are obtained, and the frame structure type comprises a first-class frame structure and a second-class frame structure; based on the modeling parameters of the first-class frame structure, generating the first-class frame structure at the plurality of target modeling nodes; and / or generating the second-class frame structure between the adjacent target modeling nodes based on the modeling parameters of the second-class frame structure. While the modeling speed of the frame structure is increased, the accuracy of the modeling position of the frame structure is considered, and the batch modeling efficiency of the frame structure is improved.

The application relates to the technical field of three-dimensional modeling, in particular to a modeling method and system based on dynamic 3D targets, an electronic device and a storage medium, the method comprising: generating a three-dimensional modeling operation instruction, wherein the operation instruction at least contains an operation type, a target object identifier and a space scope parameter; constructing an operation semantic tree based on the operation instruction; detecting a space scope overlapping area by comparing a local operation semantic tree with a received remote operation semantic tree; applying a space-time dimension priority merging rule when the overlapping area is detected; executing the merged operation instruction and generating a binary log containing an operation semantic tree hash value; and performing a local rollback based on a differential CRC check value when an operation conflict is detected, wherein the differential CRC value is only calculated for the overlapping area data block. The application effectively solves the problems of semantic understanding loss, logical inconsistency and low recovery efficiency during three-dimensional space operation.

This invention provides an intelligent modeling method and system for steel performance prediction based on LLM (Liquidity Management Model). The system includes: receiving and parsing user instructions through a natural language understanding module to generate a standardized task description; automatically generating or matching an automated model building process based on the task description through a planning and execution module; providing decision support based on a knowledge base through a knowledge recommendation module during task planning or execution; and automatically scheduling functional modules such as data access, feature engineering, and model building to complete the modeling task and output the results, based on the recommended information, through the planning and execution module. The system accordingly includes various functional modules implementing the above method. This application achieves end-to-end automated modeling through natural language interaction, automated task planning, and intelligent knowledge recommendation, significantly improving modeling efficiency, lowering the technical threshold, and enhancing model quality and versatility.

The application provides a life cycle assessment automatic modeling method based on a large language model, relates to the technical field of life cycle assessment, and comprises the following steps: multi-modal information such as text, tables and images is extracted from multi-source data such as academic literature and reports, instruction data sets are constructed in combination with LCA process knowledge, a base large model is fine-tuned, and a large language model in the LCA field is obtained. A standardization prompt is generated by using a prompt word optimizer, a product LCA model framework is generated by driving the model, list data is filled in, a process data is perfected in combination with a semantic search and correction mechanism. The semantic similarity of input and output streams is calculated to realize intelligent connection recommendation between unit processes. Finally, the structured data after verification is output to an LCA modeling software, an automatic modeling process is completed, and modeling efficiency and standardization are improved.

The invention discloses an adaptive continuous learning time-varying distribution parameter system space-time modeling method, device, equipment and medium, an initial space-time prediction model is constructed through off-line data, after entering an online stage, the space-time non-stability degree of a system is quantified in real time, and the time-varying distribution parameter system space-time modeling method is realized. A space-time forgetting factor is adaptively generated by using a multi-criterion inference mechanism, so that the updating rhythm of the model is dynamically matched with the variable-scale time-varying rhythm of system dynamics, and the problem that a fixed learning rate is difficult to adapt to multi-scale time-varying characteristics is solved; through a space-time collaborative replay learning mechanism, a disastrous forgetting phenomenon in space-time continuous learning is overcome by recall consolidation of a historical core space-time dynamic mode, and the steps of online distributed parameter system data acquisition and model dynamic updating are repeated until an online stage is ended. Accurate tracking and long-term knowledge maintenance of time-varying space-time dynamics are realized, and prediction precision and robustness of a distributed parameter system in a complex non-stationary environment are remarkably improved.

The invention discloses a BIM modeling method and device based on a large language model, equipment, a medium and a product, and relates to the technical field of data processing.The method comprises the steps that a CAD construction drawing file and a natural language instruction input by a user are obtained; converting the natural language instruction into a high-level language script according to a pre-constructed knowledge base; determining an ROI (Region of Interest) in the CAD construction drawing file according to the high-level language script; performing BIM modeling on the ROI region based on the high-level language script to obtain a BIM model; and outputting the BIM model. According to the method, the operation process is greatly simplified, and meanwhile, the BIM modeling efficiency is remarkably improved.