63 results about "Model quality" patented technology

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention relates to the technical field of large model algorithms, in particular to a large model dynamic compression optimization method and system based on sparse pruning, and the method comprises the steps: capturing original weight fluctuation data generated by resource fluctuation in reasoning, and obtaining sparse weight reference data through sparse processing; analyzing calculation complexity through model reasoning delay data, and separating reasoning delay amount caused by a model scale; dynamically controlling the model compression ratio within a preset performance range based on the delay amount and the sparse reference data, and collecting reasoning precision distribution data under different compression parameters; evaluating a model performance state under each parameter by means of a neural network simulation method, and generating a performance state simulation result; determining a model quality optimization compensation parameter based on a simulation result by combining resource fluctuation data acquired in real time in a compression process; and finally, the compression strategy is adaptively regulated and controlled through the compensation parameters, and collaborative optimization of model calculation complexity, reasoning precision and delay during dynamic change of hardware resources is realized.

The embodiment of the invention provides a quality evaluation method and device, a storage medium and a computer program product. The method comprises the steps of obtaining first data; wherein the first data comprises point cloud data corresponding to one or more Three Dimensional (3D) models, and the point cloud data comprises point cloud data corresponding to one or more Three Dimensional (3D) models; generating a quality evaluation result corresponding to the 3D model based on the point cloud data and the first model; wherein the first model comprises a first module, and the first module is at least used for converting the point cloud data into a first representation form and a second representation form; wherein the first representation form comprises a voxel grid form, and the second representation form comprises a two-dimensional (Two Dimensional, 2D) image form, that is, according to the embodiment of the invention, the quality of the 3D model can be evaluated based on the voxel grid data and the 2D image data at the same time, so that the advantages of the multi-modal data can be fully utilized, and the accuracy of the quality evaluation of the 3D model is improved.

The invention provides a CAE modeling and grid dividing method and system based on natural language driving, and the method comprises the steps: constructing a triple language model, defining an action-object-adversity triple structure, receiving a natural language instruction, carrying out the standardization processing of the instruction, recognizing and extracting the action, object and adversity components in the instruction, and carrying out the recognition and extraction of the action, object and adversity components in the instruction. The missing necessary parameters are prompted and complemented; based on a predefined analysis rule, extracting a triple conforming to the triple structure from the processed instruction; mapping the triad passing the verification into a corresponding CAE operation instruction through a mapping rule base, and generating an instruction sequence according to a logic sequence of a CAE modeling process; and automatically executing the instruction sequence, completing CAE modeling and grid division operations, and finally outputting a CAE model file. According to the method, the user operation complexity and the professional threshold are remarkably reduced, the modeling efficiency and the model quality are improved, an efficient and intelligent auxiliary tool is provided for engineering design, and the wide application value is achieved.

A gas temperature field reconstruction method using thermocouple measurement correction is disclosed. First, an Inventor 3D model and an ANSYS temperature field model are established for the gas temperature field. Then, a mapping relationship is established between the control parameters of the gas temperature field and the boundary conditions of the ANSYS temperature field model. Based on the principle of maximizing the influence of fuzzy boundary conditions on the temperature field reconstruction results, experiments are designed for precise boundary conditions and experimental data are obtained. Next, given the boundary conditions of the ANSYS temperature field model, the model is run to obtain the temperature field reconstruction results. A model quality assessment algorithm is run to calculate the fitting degree between the measured curve and the simulation curve, obtaining the algorithm fitting parameters. The algorithm fitting parameters are selected to form the loss function of the gradient descent algorithm. The learning rate, termination condition, and initial iteration parameters are determined. The gradient descent algorithm is run until convergence. Finally, the final iteration parameters are taken as the correction result of the fuzzy boundary conditions of the ANSYS temperature field model. This invention improves the spatiotemporal resolution of the temperature field reconstruction results.

The invention relates to the technical field of live-action three-dimensional modeling, in particular to a live-action three-dimensional model quality evaluation method, which comprises the following steps of: firstly, constructing a multi-dimensional evaluation index set covering dimensions such as geometric accuracy and texture quality; an initial weight is obtained through coupling of an analytic hierarchy process and an entropy weight method, and a dynamic weight is formed by combining scene demand coefficient adjustment; then, a subjective-objective linkage relation is established through grey correlation analysis, and index scores and weights are corrected through feedback coefficients; then calculating the total quality score of a single time node; the evaluation effectiveness in the dynamic scene is verified through a time sequence consistency index; and finally outputting the comprehensive quality grade. According to the method, a multi-dimensional index set is constructed to cover full dimensions, dynamic weights are calculated in combination with scene demand coefficients, and the problems of single dimension and poor scene adaptation are solved; building subjective-objective linkage by means of grey relational degree, correcting scores and weights, and solving disjunction; and calculating a time sequence consistency index to verify dynamic coherence, solving static limitation, and adapting to full-life-cycle management and control of the model.

The invention relates to the technical field of building construction, in particular to an AI-based BIM (Building Information Modeling) and design budget integration method, which comprises the following steps of: firstly, constructing a high-precision enhanced BIM by fusing laser scanning point cloud, texture data and IoT (Internet of Things) environment data with a BIM original model; then, the trained multi-task deep learning model is utilized to automatically identify defects such as equipment missing, redundancy and geometric distortion in the enhanced BIM model, and a mapping relation between components and cost items is synchronously constructed; and then, calling a predefined strategy or a generative AI to repair the model according to the defect information, and updating a bill of quantity and a budget report in real time in a linkage manner. And finally, through difference degree evaluation and reinforcement learning driven closed-loop optimization, a compliant repair scheme with the lowest cost is automatically selected. According to the method, full-process automation and optimization from defect identification and intelligent repair to cost synchronization are realized, and the model quality and the cost control precision in the design stage are remarkably improved.

The application belongs to the technical field of UML model fragment reuse, and relates to a UML model fragment reuse method, which filters out non-packable elements and external reference elements according to UML elements selected or input in other manners, recursively queries reference elements associated with valid packable elements, and completely collects all elements required to express a UML model fragment, thereby solving the core problems that the prior art cannot completely process associated metadata and reference relationships and cannot reuse UML model fragments across scenes. In addition, the UML model fragment template created is stored in an EMF resource, and relies on the standardized storage characteristics of EMF, so that the template can be conveniently managed and called across projects, and is compatible with other modeling tools. Finally, the UML model fragment is conveniently reused, and modeling efficiency, model quality and maintainability are significantly improved.

The invention relates to the technical field of material simulation, and discloses a closed-cell foamed aluminum acoustic performance simulation method based on a Voronoi model, which comprises the following steps: model construction: constructing a three-dimensional solid model of closed-cell foamed aluminum with adjustable porosity, aperture and wall thickness parameters based on the Voronoi principle; performing model optimization: performing homogenization processing on a Voronoi polyhedral structure in the three-dimensional entity model by adopting a random point homogenization algorithm so as to improve the quality of the model; and acoustic simulation: importing the optimized three-dimensional solid model into finite element simulation software, building a simulation model, and calculating the sound absorption performance or sound insulation performance of the simulation model. According to the method, the technical problems that an existing simulation model is distorted and the grid quality is poor are solved, the accuracy and reliability of a simulation result are remarkably improved by constructing the solid model which is accurate in geometry and uniform in structure, and a set of complete and verifiable technical method is provided for acoustic performance prediction and structural design of the closed-cell foamed aluminum.

The embodiment of the application discloses a model quality evaluation method, device, electronic equipment, product and storage medium, the method comprises the following steps: obtaining the source file of the building information model and the engineering construction specification information corresponding to the building information model; at least one specification entity and the association relationship between the specification entities are extracted from the engineering construction specification information to construct a target knowledge graph; information associated with at least one building component in the building information model is extracted from the source file to obtain building component association information; the building component association information is converted into knowledge structured data matching the data format of the target knowledge graph; the quality of the building information model is evaluated according to the knowledge structured data and the target knowledge graph, and an evaluation result is obtained. The scheme in the application can improve the efficiency and accuracy of the building information model quality evaluation result.

The application provides a limit test method, system, equipment and medium of an artificial intelligence model, and the method specifically comprises the following steps: constructing a plurality of scene templates for different types of games, generating a test case set by combining and transforming each scene template according to a boundary condition input by a user; based on the test case set, determining an optimal test parameter combination satisfying the artificial intelligence model by using a heuristic search algorithm according to a preset optimization objective function; automatically testing the artificial intelligence model according to the optimal test parameter combination, generating a model test result; determining the performance of the artificial intelligence model under the boundary condition by analyzing the model test result, generating a model test report, and optimizing and retesting the artificial intelligence model through the model test report. The application realizes full-process automation and intelligentization of game AI model testing, and significantly improves the test efficiency and model quality.

This invention relates to the field of large model algorithm technology, and in particular to a dynamic compression optimization method and system for large models based on sparse pruning. The method captures raw weight fluctuation data caused by resource fluctuations during inference, and obtains sparse weight baseline data through sparsification. It analyzes the computational complexity of model inference latency data to separate the inference latency caused by model size. Based on the latency and sparse baseline data, the model compression ratio is dynamically controlled within a preset performance range, and inference accuracy distribution data under different compression parameters are collected. The model performance status under each parameter is evaluated using neural network simulation methods, generating performance status simulation results. Combined with real-time resource fluctuation data acquired during compression, model quality optimization compensation parameters are determined based on the simulation results. Finally, the compression strategy is adaptively adjusted through the compensation parameters to achieve coordinated optimization of model computational complexity, inference accuracy, and latency when hardware resources change dynamically.

The invention provides an incentive and secure federal learning method and system based on a block chain, and the method comprises the steps: a plurality of participants obtain training tasks from the block chain, submit corresponding bidding prices to a smart contract, the smart contract determines a winner set in current training according to the bidding prices, and randomly divides the winner set into a training set and a verification set, obtaining a corresponding trainer and a verifier; the method comprises the following steps: downloading a currently trained local model from a block chain based on winner, adding artificial Gaussian noise, calculating a noise gradient of a target function of the local model by each winner, transmitting the noise gradient to each verifier for model verification, obtaining a list of the selected local model, and performing global aggregation; all verifiers are divided into small fragments, after a verification task of a local model of the trainer is received, each verifier in the small fragments evaluates the quality of the model, and the verifiers exchange votes mutually. The fragmentation consensus algorithm based on circulation can ensure the same security as a non-fragmentation consensus protocol.

The invention relates to the technical field of airborne system design, and discloses an airborne system model modeling rule making method and device, equipment and a medium, which can deeply fuse airborne system design multi-source heterogeneous knowledge, automatically generate executable modeling rules, improve airborne system design efficiency and model quality, and improve airborne system design efficiency and model quality. The problem that modeling rules are inconsistent due to the fact that traditional modeling rules depend on artificial experience is solved, and standardization, consistency and quality control efficiency of airborne system design are improved.

A system and method are provided for fine-tuning large language models (LLMs). The method provides high system performance during fine-tuning and attains state-of-the-art model quality on downstream applications. The method identifies the most important LLM weights via second-order information in a pre-processing step, and significantly reduces the computation and storage costs of the pre-processing step via i) a hierarchical approximation of second-order information, and ii) an online projection and rediagonalization algorithm. The method may train only the sparse important weights and embeds these sparse weights into the pre-trained LLM during fine-tuning to provide high system performance.

The invention discloses a method and equipment for improving engineering problem modeling quality and a storage medium, and belongs to the field of engineering optimization problem modeling by a large language model. The method comprises the following steps: step 1, organizing a series of engineering optimization problems according to hierarchical classification and complexity, and constructing a tree structure knowledge base; step 2, receiving natural language description of a target engineering problem to be modeled, performing recursive search on the tree structure knowledge base in the step 1, and identifying modeling sub-problems corresponding to nodes related to the target engineering problem until the most matched and most specific modeling sub-problem is found; 3, retrieving an advanced modeling thought corresponding to the modeling sub-problem in the step 2, and combining the advanced modeling thought with the description of the target engineering problem to generate a global modeling thought; and 4, automatically generating an optimization model and solver codes of the target engineering problem by using the large language model according to the global modeling thought in the step 3. According to the method, the automatic modeling quality of solving a complex engineering optimization problem by using a large language model can be improved.

Examples described herein relate to systems and methods for automatic evaluation of graphical element recommendations, such as sticker recommendations. According to some examples, a system accesses a set of text queries and provides each text query as input to a graphical element recommendation machine learning model. The graphical element recommendation machine learning model is trained to generate, based on a given text query, one or more graphical element recommendations for use in a message in a context of a messaging interface of an interaction application. The system obtains, from the graphical element recommendation machine learning model, at least one graphical element recommendation for each text query. The system generates a model quality score for the graphical element recommendation machine learning model by applying a model quality metric to the graphical element recommendations. Output indicative of the model quality score may be presented at a user device.

The invention discloses a multi-view-based three-dimensional reconstruction model quality evaluation method and system and a medium, and belongs to the field of multi-view three-dimensional reconstruction model quality evaluation. Comprising the following steps that a model generated through multi-view three-dimensional reconstruction is obtained, and the surface of the model is a Lambert body surface; the rendering equation of the Lambert body surface is converted into a point multiplication form of an incident light spherical harmonic coefficient and a surface transmission characteristic spherical harmonic coefficient, and the surface transmission characteristic is a product of a binary visible function and a geometric cosine term; the real irradiance of the surface of the reconstruction model is reversely deduced through the pixel intensity of the multi-view image; based on the incident light spherical harmonic coefficient, calculating the reconstruction irradiance of the reconstruction model by using a spherical harmonic coefficient linear combination equation; if the difference value between the reconstruction irradiance gradient and the real irradiance gradient between any two points in the model is greater than a preset threshold value, determining that a geometric error exists in a three-dimensional reconstruction result between the two points; according to the method, which parts of the reconstruction model have errors can be accurately judged.

Embodiments of the invention disclose a model quality evaluation method and apparatus, an electronic device, a product and a storage medium. The method comprises the steps of obtaining a source file of a building information model and engineering construction specification information corresponding to the building information model; extracting at least one specification entity and an association relationship between the specification entities from the engineering construction specification information to construct a target knowledge graph; extracting information associated with at least one building component in the building information model from the source file to obtain building component associated information; converting the building component associated information into knowledge structured data matched with the data format of the target knowledge graph; and according to the knowledge structured data and the target knowledge graph, evaluating the quality of the building information model to obtain an evaluation result. According to the scheme, the efficiency and accuracy of the building information model quality evaluation result can be improved.

This invention relates to a model partitioning method based on cloud-edge collaborative inference. By setting a split point, the simplified model is divided into two parts. The mobile terminal executes the first part, transmitting intermediate computational data to the edge cloud, which then completes the remaining part and returns the final result. Specifically: a simplified model library is constructed based on different compression and simplification algorithms for the original task model. Dual backups are implemented on the edge cloud and the mobile terminal. When a task arrives, the task objective is obtained, including latency, accuracy, or model quality. The communication environment is assessed, including the computing power of the edge cloud and the local computing power that can provide computational services. The parameters of each simplified model in the simplified model library are traversed: the number of network layers and the size of each layer. The performance of the simplified model is estimated according to the defined formulas: inference latency, inference accuracy, and partitioning quality. A suitable simplified model and partitioning layer are selected based on the specific task objective.

The invention relates to a SysML-based system function architecture model automatic generation and real-time synchronization method and system, and the method comprises the steps: receiving a function demand text inputted by a user through a demand analysis module, and generating a structured set containing function demand elements and attribute relationships thereof; and mapping the structured demand into a SysML activity diagram, and optimizing the node layout through a dynamic programming algorithm. The matching degree of active nodes and system modules is calculated based on a multi-objective optimization algorithm (improved NSGA-II), and automatic allocation is realized by taking module coupling degree minimization and function cohesion maximization as principles. And generating an interface definition between the system modules according to the data flow relationship. And the consistency of multi-user collaborative modeling is guaranteed by operating a conversion conflict resolution algorithm. And performing formalized verification on the generated state machine diagram by adopting a sequential logic model detection technology. According to the method, full-automatic generation and real-time synchronization of the model from the demand to the complete system function architecture model are realized, and the modeling efficiency, the model quality and the collaborative reliability are remarkably improved.

The invention discloses a tool life DOE analysis response generation method based on wear feature extraction and fusion, a tool life regression analysis method, m rated cutting distances are set, a tool is defined to be provided with n cutting edges, m and n are positive integers greater than or equal to 1, and the method comprises the following steps: when a rated cutting distance is reached, the tool life DOE analysis response is generated; the wear values of all the cutting edges at the moment are obtained, the wear values are weighted according to the rated cutting distance, m * n weighted values are summed, the VB weighted sum is obtained, and the VB weighted sum serves as the response of DOE analysis. According to the method, the VB weighted sum is used for replacing the traditional method that VB with a single rated cutting distance is used as the response Y of DOE to carry out regression fitting model analysis, so that the quality of the model can be effectively improved, and the optimal X (tool design factor) combination of the lowest VB weighted sum on the whole life cycle can be obtained.

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 invention provides an anti-collusion verifiable federated learning client selection method, and the method comprises the steps: generating a verifiable public random seed through the cooperation of a client based on a current training round; the client local model updating is completed, dimension reduction is performed on the client local model updating through a public random projection matrix, a model parameter commitment is generated in combination with a private random number, and the model parameter commitment is broadcasted to all participating clients and servers; calculating the selected weight of the client in the current round based on the model quality factor and the long-term fairness factor; based on a zero-knowledge proof system, the verification weight is obtained through calculation according to a preset rule based on committed model parameters; collecting all effective weights, and performing normalization processing to generate effective normalized weights; and based on the public random seed and the effective normalized weight, adopting an unbiased sampling algorithm to select a client.

This invention relates to the fields of blockchain and privacy computing technology, and provides a blockchain-based federated learning method, an industrial product quality prediction method, and a system. The federated method includes: deploying an initialized global model on the blockchain; clients retrieving the model from the chain, training it locally, and submitting model updates; committee nodes, dynamically elected, scoring the updates based on a verification dataset, and generating a consensus score by combining reputation weights and a two-sided pruning mechanism; qualified updates being aggregated by a smart contract to generate a new global model and writing it back to the blockchain; the smart contract distributing rewards and updating the reputation of the client and committee members based on the client's model quality and the consistency of committee member votes; and the system entering the next training iteration. This invention effectively solves the problems of centralized dependence, low-quality update interference, and unfair incentives in traditional federated learning by introducing an incentive mechanism and a dynamic reputation evolution mechanism.

The application provides a test model mass property matching design method considering component motion, which establishes a test model, a mass center relationship model of fixed components and moving components and a moment of inertia relationship model in component linear motion, calculates the mass, real-time position of the mass center and the moment of inertia relative to the mass center of the moving components based on the model, designs an entity test model based on the obtained mass properties, obtains the actual value, the theoretical value of the combined mass center position and the actual value, the theoretical value of the total moment of inertia of the moving components at the starting position, the ending position and the typical intermediate position based on the entity test model and the theoretical design model respectively, designs the mass properties of the counterweight by analyzing the difference between the actual value and the theoretical value, and realizes real-time simulation of dynamic changes of the mass properties of the entity test model to the theoretical design model in the component motion process. The technical scheme of the application solves the technical problem that the test model is difficult to accurately simulate the dynamic changes of the mass properties of the theoretical design model in the prior art.

The present disclosure provides a model training method, an image editing method, an apparatus, a medium and an electronic device for image editing, relating to the technical field of artificial intelligence. The model training method comprises: processing a first sample image by using an encoder to obtain a sample original feature corresponding to the first sample image; inputting the sample original feature into an editing model to be trained, editing the sample original feature according to a target text by using the editing model to obtain a sample editing feature; processing the sample editing feature by using a decoder to obtain a sample editing image corresponding to the sample editing feature; determining a first loss function value by matching the sample editing image with the target text; and updating parameters of the editing model according to the first loss function value. The present disclosure reduces the data acquisition cost, is conducive to improving the model training effect and improving the model quality.

The invention provides a geometric model processing method for airflow organization simulation, relates to the technical field of geometric model processing, and aims to solve one of the technical problems that defects are difficult to find, the processing efficiency is low and the subsequent grid division quality is unreliable during manual inspection of a geometric model. The method comprises the following steps: automatically screening and retaining target graphs and information, and deleting other irrelevant geometries; when the geometric model is a two-dimensional model, performing surface domain restoration on the two-dimensional model; when the geometric model is a three-dimensional model, diagnosis and repair are carried out for the situation that sharp corners, small gaps, face-to-face small distances, body-to-body intersection and the like in the geometric model have small influences on the flow field, but influence on the number of later grids and the quality of the grids; checking the repairing quality of the geometric model item by item, and if the coordinate information of the target graph and the geometric parameters of the geometric model do not meet preset conditions, adjusting a repairing threshold value or iteration parameters to carry out iterative repairing until the geometric model meets the preset conditions. The method improves the model processing efficiency and the model quality.

The present disclosure provides a business model creation method and device, the method comprising: in response to a triggering operation on a modeling control in a visual modeling page, displaying a modeling element area; in response to a configuration operation triggered on each modeling element in the modeling element area in sequence, configuring modeling parameters corresponding to each modeling element based on a target business requirement; in response to the modeling parameters of each modeling element being configured, generating a target modeling process matched with the modeling parameters based on the modeling parameters configured for each modeling element; and running the target modeling process to execute a model construction operation through multiple modeling process nodes to generate a target business model. Thus, the present disclosure reduces the threshold for non-technical users to use machine learning modeling tools, reduces the dependence on feature engineering professional knowledge, and avoids the model failure problem caused by node misconfiguration or process loss in traditional visual modeling, while ensuring model quality and significantly improving modeling efficiency.

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.