187 results about "Model quality" patented technology

Methods and systems for source machine learning (ML) model selection for the transfer learning. A method may include receiving a source ML model request from a target domain, determining candidate source ML models, calculating a model quality score for each of the candidate source ML models, using the calculated model quality scores to select candidate source ML models, sending the selected candidate source ML models to the target domain, receiving fine-tuned ML model weights for fine-tuned ML models, and calculating a model quality score for each of the fine-tuned ML models. The method may include determining, for each of the fine-tuned ML models, a ranking and / or a deployment recommendation for the fine-tuned ML model based on the model quality score for the fine-tuned ML model and sending, for each of the fine-tuned ML models, the ranking and / or the deployment recommendation for the fine-tuned ML model to the target domain.

The invention discloses an AI quality control system based on digital twinning, and relates to the technical field of injection molding product production, and the system comprises a data acquisition module, a digital twinning modeling module, an AI quality detection module, a quality analysis module and an environment optimization module. The data acquisition module collects production and environment data in real time, the digital twin modeling module obtains a high-precision model by simulating the production process, and the AI quality detection module compares simulation data with standard data, calculates a forming deviation coefficient and adjusts process parameters. The quality analysis module detects the injection molding defect risk through the stress concentration coefficient, and the environment optimization module monitors the production environment and provides an optimization strategy when an early warning instruction is triggered. The system can monitor the production process in real time, dynamically adjust technological parameters, improve the production efficiency and the product quality and reduce the rejection rate.

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 provides a limit test method, system and device for an artificial intelligence model and a medium, and the method specifically comprises the steps: constructing a plurality of scene templates for different types of games, and generating a test case set by combining and transforming the scene templates according to a boundary condition input by a user; based on the test case set, according to a preset optimization objective function, adopting a heuristic search algorithm to determine an optimal test parameter combination meeting the artificial intelligence model; performing automatic testing on the artificial intelligence model according to the optimal test parameter combination, and generating a model test result; and analyzing the model test result, determining the performance of the artificial intelligence model under the boundary condition, generating a model test report, and carrying out optimization and re-test on the artificial intelligence model through the model test report. According to the method, full-process automation and intellectualization of game AI model testing are achieved, and the testing efficiency and the model quality are remarkably improved.

The invention discloses a compiling, checking and reviewing method and system for a three-dimensional model of a three-dimensional factory. The method comprises the steps that a three-dimensional factory design cooperative working platform conducts checking before checking and reviewing on the three-dimensional model and generates a checking report; in response to an operation instruction of a compiling and checking person, the platform submits a model checking requirement and a connection relationship between an item bottom layer and a database in the three-dimensional model to the checking person for checking, and meanwhile, the model checking requirement and the connection relationship can be returned to the modeling person for modification, and operation is repeated until checking is passed; and finally, the platform locks the checked, edited and revised three-dimensional model and stores the three-dimensional model in a platform database. According to the method, the workload of modeling personnel can be reduced, the quality of the three-dimensional model can be controlled through a platform tool, the quality of the model can be controlled step by step in detail, meanwhile, the three-dimensional model is locked all the time in the verification process, the three-dimensional model can be modified only at the modeling personnel, and the verification efficiency is improved. And the consistency of the three-dimensional model and the drawing is further ensured.

The invention discloses an enterprise credit evaluation method based on a block chain and federated learning in house transaction. The method comprises the following steps: a task issuer issues an evaluation task; the client performs model training by using local data; the task publisher evaluates client integrity according to a training result and distinguishes honest and malicious clients; selecting a committee node and a leader node from the honest client; the committee node performs cross validation by using local data, and evaluates the accuracy of the model; the leader node adaptively updates an aggregation weight based on client honesty, model accuracy and data scale, aggregates a global model and generates a block, and uploads the block after consensus by an improved PBFT protocol; and the next round of training is carried out until the global model meets the task requirement. According to the method, credible data is utilized to guide model updating, the influence of a malicious client side is reduced, a PBFT consensus mechanism is improved, the consensus efficiency is improved, aggregation weight is adaptively updated based on multiple factors, and the model quality under the condition of data heterogeneity is guaranteed.

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 invention provides an intelligent photocuring 3D printing model optimization method, and relates to the technical field of photocuring 3D printing, and the method comprises the following steps: obtaining a 3D printing model file uploaded by a user; analyzing geometric information in the model file, and extracting a triangular grid structure, normal vector information and a surface area; key feature areas of curved surfaces, corners and edges in the model are identified, printing parameter data are collected, and a target model summary file is generated; an optimization printing target is defined, an optimization model is generated based on the generative adversarial network, model quality is evaluated through a discriminator network, a multi-target optimization result is analyzed, and an optimal balance point among different targets is found; and testing the optimized model and the printing parameters by using virtual reality, and performing self-adaptive correction according to a simulation test result to adjust to the optimal printing parameters. According to the method, the geometric features and the material attributes of the 3D model are automatically analyzed, potential weaknesses in the model are identified, and the 3D printing model is intelligently optimized.

The invention provides a model quality evaluation optimization method and device for industrial simulation application and a server, and relates to the technical field of simulation application generalization development, and the method comprises the steps: obtaining a to-be-processed geometric model, and carrying out the format detection processing, format conversion processing and asynchronous processing of the geometric model, and determining a target input model; performing grid division processing and finite element analysis processing on the target input model, determining a finite element calculation result, performing local error estimation analysis processing on the finite element calculation result, locally refining a model grid of the target input model, determining a model key region, performing local encryption processing on the key region, and obtaining a local encryption result of the target input model; determining a model output result; and carrying out optimization processing on the model output result through a model quality evaluation optimization system with a layered architecture, and determining an optimized target model output result. The model quality can be remarkably improved.

According to the BIM-based parameterized spatial steel truss structure modeling method, electronic drawing spatial steel truss data are extracted through simple operation, an Excel table is processed and generated, spatial steel truss node three-dimensional coordinate data are extracted, precision errors caused by human factors are eliminated, meanwhile, a Revit template file is created, and a BIM-based parameterized spatial steel truss structure modeling method is provided. According to the method, a self-adaptive rod piece and bolt-sphere joint parameterization family is established, the modeling efficiency of the space steel truss in Revit is effectively improved, at least 90% of modeling time is saved, a running program written by Dynamo is applied, table data and parameterization family data are automatically called, accurate space steel truss and bolt-sphere joints are automatically generated, and the modeling efficiency and the model quality are greatly improved.

The invention discloses a deep learning table recognition model evolution-oriented regression defect relieving method, which comprises the following steps of: firstly, screening new features by applying a CONMI evaluation function in combination with specific hyper-parameters in a feature selection stage, and taking a new feature set obtained by combining the new features with an old feature set as model training data to realize contribution degree control-based retraining; on one hand, the feature contribution degree is calculated through the expected gradient, on the other hand, an overall loss function containing cosine similarity and a cross entropy loss function is designed to stabilize the old feature contribution degree and learn new features, and a model is trained through forward propagation and back propagation processes based on a new feature set; and under the condition that the accuracy rate of the table recognition model of the new version meets the condition, constructing a feature evolution scene, realizing a model evolution process according to a regression mitigation strategy, and taking the accuracy rate of the table recognition model of the new version as an evaluation index to obtain an evolution process regression defect mitigation strategy. The method is beneficial to improving the quality and reliability of a deep learning table recognition model.

A privacy protection heterogeneous federal learning method with Byzantine robustness includes training a local model and calculating a sketch, removing an abnormal local model, selecting a client with quick response capability, encrypting local update and submitting ciphertext, executing weighted aggregation and distributing an updated global model. The method has the beneficial effects that a high-dimensional local model is converted into a low-dimensional sketch by adopting locality sensitive hashing, and the quality of the local model is effectively evaluated on the premise of protecting data privacy. Then, the Byzantine clients are identified and removed based on the hierarchical clustering technology, aggregation weights are distributed to the remaining clients according to the local model quality, and the global model convergence speed is increased; in addition, by selecting the clients which are high in response speed and have representative data sets to participate in model training, the problem of outdated persons caused by system isomerism is solved under the condition that the global model accuracy is not affected.

The invention relates to a dynamic multi-dimensional quality evaluation system and method for a thermal power plant intelligent inventory supervision model. Real-time data flow of the DCS is collected and compared with the model prediction value, and a prediction error is generated; based on the load rate, the fuel characteristic and other characteristics, the current operation state is matched to a preset working condition library, and historical data is called for index calculation; the method comprises the following steps of: according to stability, reusability and economic indexes such as a variable coefficient of a prediction error, a model calling frequency, economic benefit influence and the like, normalizing the indexes by adopting a range method, and calculating the weight of each index by utilizing an entropy weight method; constructing a weighted standardization matrix, defining a dynamic ideal solution, calculating a close degree index, and outputting a comprehensive evaluation result; according to the scoring result, an optimization instruction is automatically pushed, and model parameter adjustment or data calibration is carried out, a set of dynamic and multi-dimensional thermal power plant supervision model evaluation system is constructed, the model quality can be objectively and comprehensively evaluated, and a closed-loop optimization mechanism is provided.

The present invention relates to the data updating technology, disclosing a method for constructing a quality evaluation data updating model based on a convolutional neural network including: collecting historical data of product quality evaluation from an original quality evaluation model, determining the update frequency of quality evaluation data of original model; obtaining the latest quality evaluation data for data update of model based on the historical data of product quality evaluation; establishing a first data sample set and a second data sample set to update the model; and conducting model performance testing on the original model with such updated data to determine the effectiveness evaluation results of the data updates. The present invention updates the data samples of the quality evaluation model by determining the data update frequency of the model, and improves the performance and accuracy of the model by evaluating the effectiveness of the data updates.

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 quality control predictive analysis method and platform, and belongs to the technical field of predictive evaluation, and the method specifically comprises the steps: carrying out the evaluation processing of the quality score of an updated credit model in a predictive analysis period, so as to update the evaluation analysis results of the quality scores of the updated credit model in different predictive analysis periods, according to the control method for determining the model quality of the credit loan model, the matching condition of the credit loan model in a prediction analysis period between different update processing times is combined with the control method for the model quality of the credit loan model with different update processing times. And a prediction analysis method for quality control after the next update processing of the credit model is determined, so that the model quality of the credit model after the update processing is improved.

The invention discloses an automatic driving-oriented personalized federal learning and communication method, which is characterized in that local model and global model aggregation is carried out based on a parameter similarity adaptive algorithm, weighted aggregation is carried out by measuring the weight similarity of personalized local models uploaded by clients, and the problem of data isomerism is solved. And secondly, compressing the local model to obtain a teacher model and a student model, and cooperatively optimizing the teacher model and the student model through mutual distillation mechanism bidirectional knowledge migration to solve the problem of overlarge model quality and precision and communication overhead. And finally, model parameters are compressed based on singular value decomposition, so that a communication mode in personalized federated learning is constructed, and the method can be suitable for various complex environments.

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.

In one embodiment, a method includes determining which objects from a substantial dataset are expected to lead to the largest increase in model quality by applying a samples-selection algorithm using computational capability comprising a processor and / or a memory (e.g., of a processing system and / or a graphics processing unit). The aspect quantifies an informativeness score of data elements in the substantial dataset to determine how likely and / or by what degree data elements will lead to model improvement. The method then automatically determines which data elements of the substantial dataset are in need of human annotation based on a prioritization order derived from the informativeness score and chooses a selected data based on the automatically determining which elements of the substantial dataset are in need of human annotation based on the prioritization order derived from the informativeness score. The method then matches the selected data to an expert.

Systems and methods are disclosed for training a Natural Language Processing (NLP) model through iterative processing. A method involves performing multiple iterations to train the NLP model. Each iteration includes defining data points with assigned labels and preparing natural language queries for these points. A neural network processes real-world documents to generate extracted values from the real-world document to be used for querying. The extracted values are then validated by comparing them to the document data and identifying validated extracted values. The NLP model quality is evaluated using these validated values, and a quality score is determined. Upon identifying a suitable quality, the NLP model is defined as a fine-tuned model and configured to process new real-world documents for data point extraction.

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 a model training method and device, a target detection method and device and electronic equipment, and belongs to the technical field of neural network models.The model training method comprises the steps that a non-target domain training data set and target domain data are obtained, the non-target domain training data set is adopted to train migratable features of a to-be-trained model, and the to-be-trained model is obtained; obtaining a pre-training model; processing the target domain data by adopting the pre-training model to obtain a pseudo tag of each target domain data, and generating a target domain training data set based on each target domain data and the pseudo tag; and carrying out multi-batch training on the pre-training model by adopting the target domain training data set to obtain a trained model. According to the method, a large amount of high-quality target domain training data can be obtained, the model is trained, the quality of the model is improved, the model is trained through the non-target domain data with the label, and tasks such as identification and classification of the target domain data without the label can be achieved.

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.

A set of building-oriented ship three-dimensional model modeling quality inspection set system is systematically constructed based on a 3DE platform, a corresponding model quality inspection method is provided for each inspection item, a set of three-dimensional model modeling quality inspection system is formed, information quality inspection for a ship product three-dimensional model is realized, and the inspection efficiency of the ship product three-dimensional model is improved. A designer is helped to improve the quality and efficiency of product design, discover model quality defects, reduce design repetition and promote ship product quality improvement.

The invention provides a test model quality attribute matching design method considering component motion, which comprises the following steps of: establishing a test model in component linear motion, a mass center relation model of a fixed component and a moving component and a rotational inertia relation model; calculating the mass, the mass center real-time position and the rotational inertia relative to the mass center of the moving part based on the model; designing an entity test model based on the obtained quality attributes, and obtaining the actual value and the theoretical value of the combined mass center position and the actual value and the theoretical value of the total rotational inertia of the moving part at the starting position, the ending position and the typical middle position based on the entity test model and the theoretical design model; the mass attribute of the balancing weight is designed by analyzing the difference between the actual value and the theoretical value, and real-time simulation of the physical test model on the dynamic change of the mass attribute of the theoretical design model in the component movement process is achieved. By applying the technical scheme, the technical problem that in the prior art, it is difficult for a test model to accurately simulate dynamic changes of quality attributes of a theoretical design model is solved.

Systems and methods are provided for implementing soft model assertions (SMA) system and techniques designed to monitor and improve Machine Learning (ML) model quality by to detecting errors within the one or more ML models. SMA techniques and systems are distinctly designed to leverage: 1) a user's ability to specify features over data; and 2) large, existing datasets of organizations, in a manner that can improve the accuracy and quality of predicting potential errors in Machine Learning (ML) models. A SMA system can include a controller device receiving predictions generated based on the ML models and output from the SMA system. The controller performs autonomous operations of the system in response to determining that the one or more detected errors within the one or more ML models yield a high certainty of errors in the predictions. The SMA system also includes a domain specific language and a severity score module.

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 present invention provides a quality control prediction and analysis method and platform, which belongs to the field of prediction and evaluation technology, and specifically includes: evaluating the quality score of the credit model after the update processing in the prediction and analysis period, determining the model quality control method of the credit model based on the evaluation and analysis results of the quality score of the updated credit model in different prediction and analysis periods, determining the prediction and analysis method for quality control of the credit model after the next update processing based on the matching of the prediction and analysis periods between different update processing times of the credit model, and combining the model quality control method of the credit model at different update processing times, thereby improving the model quality of the credit model after the update processing.

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.