101 results about "Modelling methods" patented technology

The invention relates to the technical field of building information modeling, and discloses a parameterized BIM modeling method, which comprises the following steps: step 1, acquiring specific design parameters of a modeling object in a project design requirement, marking key geometric parameters of the object, including length, width, height, volume and material characteristics, and defining units, physical meanings and application ranges of the parameters in a marking process, a basis is provided for subsequent entity model construction; and 2, based on the geometric parameters marked in the step 1, a solid model is constructed through a BIM modeling tool, and the solid model is a visual model with the shape, the size and the position determined through the geometric parameters in a three-dimensional space. The three-dimensional BIM model is dynamically generated and modified by modifying geometric parameters, model construction is more flexible and efficient due to introduction of parameterization logic, automatic adjustment and rapid iteration of the model are achieved, and the reusability of BIM modeling and the design efficiency of the building model are remarkably improved.

The invention discloses an automatic building energy consumption modeling method based on a large language model, and the method comprises the steps: obtaining a natural language demand description inputted by a user side, and carrying out the domain semantic analysis of the demand description based on a natural language model, and extracting a modeling parameter; the demand description comprises architectural space attributes; the modeling parameters are input into a building large language model, and a standardized modeling instruction set meeting the grammar specification of the target building energy consumption simulation engine is generated; wherein the building large language model is obtained by finely adjusting a pre-trained large language model through a professional question and answer corpus in the building field; an interface of the target building energy consumption simulation engine is called, a simulation instruction is generated, and the simulation instruction is used for indicating the target building energy consumption simulation engine to output a simulation result based on the standardized modeling instruction set; the simulation result comprises the building model file and a key index of the building model file. By adopting the method, the automation degree of building modeling can be improved, manual intervention is reduced, and the modeling threshold is lowered.

The invention provides an interactive BIM modeling method and system based on a large model. The method comprises the steps that a target tool set description document corresponding to a target project class is determined; receiving a task instruction input by a user, and performing task splitting by the project class large model according to a task intention of a currently executed task to obtain at least one subtask; the calling project class large model determines a tool calling instruction according to the task intention of the currently executed subtask and the target tool set description document; the tool calling instruction is sent to a tool calling module, and the tool calling module carries out compliance verification on the tool calling instruction; the tool calling instruction is processed, an execution instruction of the BIM application is generated, an execution result is received and returned to the project class large model, and the project class large model presents the execution result to a user. According to the method disclosed by the invention, the BIM modeling task is completed through simple natural language input, the operation complexity can be reduced, and a designer is helped to complete the modeling task more efficiently.

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 relates to the field of nuclear energy system simulation, in particular to a modeling method, system, equipment and medium for a nuclear energy heat supply system, which comprises the following steps: according to the topological structure of the nuclear energy heat supply system, the action of each equipment in the conversion process of nuclear energy and heat energy, the component and interface structure in each equipment, and the component and interface structure in each equipment; decomposing the data into subsystems, equipment, components, basic equations and basic physical elements; establishing corresponding basic models for the basic equations and the basic physical elements, and establishing corresponding component models, equipment models and subsystem models for the components, the equipment and the subsystems; and according to the topological structure, coupling the basic model, the component model, the equipment model and the subsystem model to construct a simulation model of the nuclear energy heat supply system. Based on the unified modeling language, interface adaptation work caused by interface incompatibility in an existing method is avoided, the modeling process is greatly simplified, and the modeling simulation efficiency of the nuclear energy heat supply system is improved.

An air conditioning system operation strategy modeling method based on a mechanism framework + AI comprises the steps that climate data are obtained and preprocessed, standardized climate data are obtained, and preprocessing comprises missing value filling processing and normalization processing; and iteratively clustering the standardized climate data through a quantum harmonic oscillator model to generate similar climate characteristic region groups. And a convolutional neural network is called to be combined with a YOLO algorithm to perform training learning according to the climate data samples in the similar climate characteristic region group and the control strategy corresponding to each climate data sample, and an operation strategy model of the air conditioning system is established. Wherein the climate data at least comprises temperature data, humidity data and wind speed data, the convolutional neural network is divided into a teacher model and a student model, the teacher model is a pre-trained model obtained by pre-training based on the climate data, and the student model is a new task model for interactive learning with the teacher model.

The invention discloses a power system component dynamic modeling method based on an MMOE-DAE model, and belongs to the technical field of electrical digital data processing, high precision and three-phase unified modeling of a power system dynamic component: the MMOE-DAE can realize accurate modeling of a complex high-order dynamic component in a power system in the absence of a detailed physical model. By integrating the neural ODE and the automatic order lifting mechanism, the modeling ability of the dynamic behaviors of the complex store assembly in various disturbance scenes is effectively improved, and it is ensured that the method still has high precision and robustness under the conditions of strong nonlinearity and high-order dynamic. The unified modeling of the three-phase circuit can process the dynamic characteristics of each phase circuit at the same time under the same framework, and processes the coupling relationship between phases through a multi-task learning mechanism, thereby significantly reducing the error caused by phase separation processing in the traditional modeling method, and improving the overall simulation efficiency.

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 technical field of BIM building modeling processing, and discloses a BIM intelligent building modeling method and system based on a large language model, and the system comprises a BIM building modeling object analysis module, a BIM building modeling instruction analysis module, and a BIM building modeling execution module. Performing BIM building modeling object three-dimensional model intelligent search according to a building modeling object type analysis result in combination with an intelligent recognition algorithm and different types of building modeling object three-dimensional model information stored based on big data, and realizing scientific and efficient generation of a BIM building modeling object three-dimensional structure based on a natural language; bIM building modeling control instruction intelligent matching is carried out according to the modeling demand text data of the position, shape and size relation between a building modeling object and a building modeling scene in combination with an intelligent search algorithm and standard-stored building modeling control instructions with different modeling demands; bIM building modeling control instructions are intelligently and accurately selected based on natural languages and design concepts of designers.

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 relates to the technical field of cognitive diagnosis, and discloses a differential chemical condition modeling method based on long-term and short-term interaction data, and the training process of a cognitive model comprises the steps: arranging question samples composed of interaction data and answering results of students on each question into learning records of the students; constructing a recent learning record and a long-term learning record based on the learning record; the recent learning records are sent to a static knowledge tracking module for learning modeling, and recent expressions of the students are obtained; the long-term learning record of the student is sent to a dynamic knowledge tracking module, and the historical cognitive state of the student is captured; obtaining the current cognitive state of the student through a fusion gating network, and predicting the probability that the student correctly answers the question; and taking the cross entropy between the probability and the actual answer result as a loss function for training. According to the method, the cognitive level of the students can be reflected, the future learning achievements of the students can be predicted, and a scientific basis is provided for educational decision making.

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.

To provide a modeling device and a modeling method, which can improve modeling accuracy.SOLUTION: A modeling device is a modeling device that performs modeling using a modeling material, comprising: a nozzle for ejecting the modeling material; a modeling table including a mounting surface for mounting the modeling material ejected from the nozzle; and an acceleration mechanism for applying acceleration to the modeling material ejected from the nozzle in a direction toward the modeling table.SELECTED DRAWING: Figure 1

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 relates to the technical field of human body modeling, in particular to a cross-domain human body unified modeling method and device, equipment and a storage medium. The method comprises the following steps: representing the relative similarity between any two skeleton postures of a human body by using a relative posture similarity space, extracting the relative similarity between an unsampled skeleton posture and an anchor prompt from the relative posture similarity space, screening out a target posture from the unsampled skeleton postures on the basis of the relative similarity, and extracting the target posture from the unsampled skeleton postures. And updating the anchor prompt set by using the target attitude to obtain a final anchor prompt set. And finally, the anchor prompts and the soft prompts in the final anchor prompt set cooperate with each other to complete the reasoning tasks of the model, and the soft prompts are parameters generated when the model is trained based on each reasoning task, so that when different reasoning tasks are processed, only the soft prompts corresponding to the reasoning tasks need to be replaced, and the reasoning tasks can be processed. Therefore, the reasoning task can be completed in cooperation with anchor prompt without replacing the model. Therefore, cross-task unified modeling can be realized.

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 relates to a modeling method of a water chilling unit extrapolation model based on physical constraint width learning and priori knowledge, which comprises the following steps: based on equipment parameters and actual operation data, applying a width learning algorithm, establishing a width learning model, and carrying out preliminary prediction on an operation state; based on the actual operation data and the energy conservation law, calculating the mean square error of the cooling water outlet temperature of the width learning model, the mean square error of the electric power of the water chilling unit and the energy loss function of the water chilling unit, and establishing a self-defined physical loss function; an error back propagation mechanism and an Adam optimizer are utilized to optimize a self-defined physical loss function, weights and bias coefficients of a feature layer and an enhancement layer in a width learning algorithm are updated, the precision of a width learning model is improved, and a physical constraint width learning model is established; establishing a priori knowledge model; and combining the priori knowledge model with the physical constraint width learning model, and establishing an extrapolation model based on physical constraint width learning and priori knowledge.

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.