379 results about "Model validation" patented technology

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention discloses a coal rock fracture intelligent extraction method based on improved U-Net. The method comprises the following steps: S1, constructing a coal rock fracture CT image data set; s2, constructing an improved U-Net segmentation model, specifically comprising the following steps: S2.1, taking VGG16 as a backbone network, and introducing a depth separable convolution module; s2.2, a PPA attention module is added after each layer of depth separable convolution of the decoder, the PPA attention module is introduced after each up-sampling stage of the decoder, and the output of the PPA attention module is subjected to batch normalization and Dropout layer processing; s2.3, defining a composite loss function; s3, training and optimizing a segmentation model, wherein the specific steps comprise: S3.1, setting hyper-parameters; and S3.2, training the model by using the training set, adjusting hyper-parameters by using the verification set, and evaluating the performance by using the test set, wherein the evaluation indexes comprise MIoU, MAcc and FWIoU. According to the method, the problems of difficult identification of small fractures, large model calculation amount, poor multi-scale information fusion and class imbalance in the coal rock fracture image can be solved, and the robustness, segmentation precision and practicability of the model are improved.

The invention discloses a diversified recommendation method based on a graph neural network and reinforcement learning. The method comprises the following steps of: preprocessing collected data containing users and things to form a data set, and dividing the data set into a training set, a test set and a verification set: mapping the users and things in the data of the training set to a dense vector space through a lookup table of an embedded layer, and selecting the data of the users and things through a facility site selection method; iteratively screening an object with the highest dependency from the vector representation embedded by the embedding layer, and constructing a diversified neighborhood subset of each user; on the basis of the constructed neighborhood subset, performing secondary neighbor selection by using an A3C algorithm, dynamically adjusting a recommendation strategy through continuous interaction between the user and the environment, capturing and adapting to the change of user interest, and performing secondary neighbor selection; inputting a selection result of the secondary neighbor selection module into a graph neural network model for processing, and training the model; and verifying the trained model by using the verification set, and performing model performance evaluation through the test set. The diversity and adaptability of recommendation can be improved.

The invention relates to a power system digital twin model generation method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of obtaining multi-source heterogeneous data of a transformer substation, preprocessing the multi-source heterogeneous data to obtain preprocessed data, performing fuzzification processing based on the preprocessed data, extracting a fuzzy logic rule, constructing a to-be-trained adversarial network model, and training the to-be-trained adversarial network model based on the fuzzy logic rule. And when it is determined that the model verification conditions are met, training is stopped, and the digital twin model of the power system is obtained. In the modeling process, a model combining fuzzy logic and a generative adversarial network is introduced, and through dynamic rule base management and self-adaptive updating, it is ensured that the system can adapt to the complex operation environment of a power system in real time, so that the accuracy of automatic generation of the substation operation order is greatly improved, and the automation degree of the substation operation order is improved. And meanwhile, the rule compliance and the compatibility of the power system standard are ensured.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention belongs to the technical field of water supply network water quality monitoring, and provides a water supply network trihalomethane prediction method based on a fusion model. Comprising the steps of original water quality data set acquisition, data preprocessing, CNN-BiLSTM-MultiHead Attention fusion prediction model construction, hyper-parameter optimization space and model evaluation function definition, model prediction and output evaluation, model parameter iteration and verification evaluation, model verification, sample importance display, to-be-detected water quality data set collection and trihalomethane prediction. According to the method, the convolutional neural network, the bidirectional long-short-term memory network and the multi-head attention mechanism are fused, so that the change characteristics of a plurality of conventional water quality indexes in the water supply network are efficiently learned and extracted, and better prediction precision can still be kept under the condition of less training data; and the prediction performance and generalization ability of the model in the aspect of predicting a plurality of target variables by using a plurality of input variables are remarkably improved.

The invention provides a landslide disaster dynamic monitoring method based on a time sequence and a hybrid model, and belongs to the technical field of intelligent monitoring, and the method comprises the steps: collecting multi-source heterogeneous data of historical landslide, carrying out the preprocessing of the multi-source heterogeneous data, obtaining first data, carrying out the type division of the first data, and carrying out the time sequence coding of each type of data, obtaining single samples under the same time sequence to obtain a sample set, and training the neural network model according to the sample set to obtain an initial model; performing model verification on the initial model, and optimizing to obtain a time sequence and a hybrid model in combination with the constructed mapping relationship between the landslide deformation and the monitoring dimension under the corresponding type; and monitoring and acquiring current heterogeneous data of the target monitoring point, inputting the current heterogeneous data into the time sequence and the hybrid model, predicting to obtain a current disaster result of the target monitoring point, and outputting a prompt. The accuracy of the model is ensured through model verification and mapping relation construction, and a convenient basis is provided for landslide disaster prediction.

The invention relates to an optimization method for intelligent ISO drawing output based on an external platform. The optimization method comprises the following steps: S1, importing a model and judging a model type; importing a model, and judging whether the model is a two-dimensional model or a three-dimensional model; s2, verifying the three-dimensional model: verifying the three-dimensional model; s3, model data splitting: splitting the verified three-dimensional model; s4, generating a pipeline component file; s5, processing the generated PCF file through an ISO # algorithm; s6, generating an ISO map of the pipeline according to the data processed by the ISO # algorithm; s7, generating a unique reference number and coordinates; S8, establishing data version management, and establishing a data version management mechanism; s9, when the three-dimensional model is modified, automatically updating the two-dimensional model data and the ISO map; and S10, auditing and publishing: auditing the generated ISO map. By efficiently processing two-dimensional and three-dimensional model data in an engineering project, automatic generation of the ISO drawing is achieved, the method is widely applied to the field of CAD design and the like, and the working efficiency and the drawing accuracy are improved.

The invention discloses a turnout switch machine fault diagnosis and prediction model method based on artificial intelligence. The method comprises the following steps: step 1, data acquisition; step 2, data preprocessing; step 3, feature extraction; step 4, model training; 5, verifying the model; 6, deploying the model; step 7, fault diagnosis and prediction; an artificial intelligence algorithm is introduced to automatically analyze data and identify faults, safe operation of equipment is ensured, instant fault response is realized through real-time monitoring and rapid data processing, the equipment safety is improved, an automatic modeling tool is adopted to simplify the construction process, non-professional personnel operation is facilitated, system deployment is accelerated, and the system reliability is improved. The method combines expert experience and data driving to form a hybrid model, improves the adaptability and accuracy of the model, integrates multi-sensor data to form a unified monitoring platform, enhances the comprehensiveness and accuracy of fault diagnosis, and provides powerful guarantee for the safety and efficiency of railway transportation.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention discloses a verifiable privacy protection federated learning method based on sensitive samples, and relates to the field of fault diagnosis. According to the method, the Poisson sampling process is introduced, the sampling probability is generated based on the privacy budget, it is ensured that all recorded privacy budgets are synchronously exhausted, data disastrous forgetting is effectively prevented, and the model effectiveness is improved. In the model verification module, a sensitive sample set is generated by means of a gradient maximization algorithm, a model integrity attack is detected, a user side only needs to submit a small number of sensitive samples for prediction through an inference service API provided by a private cloud client side, and if a returned result and a real result have significant deviation, it can be judged that the model is possibly tampered. According to the method, diversified privacy requirements of users can be met, black box verification on the integrity of the model is realized, and the precision of the model is improved.

A complex system uncertainty digital model simulation verification method based on data feature measurement comprises the following steps: under the condition of considering uncertainty, extracting feature vectors of simulation output and real installation test output parameter data from multiple angles to construct a feature matrix; and the statistical similarity between the corresponding digital model and the real installation data feature vector is measured through the probability distribution difference, so that the consistency degree between the digital model and the real installation test data is obtained. According to the method, the problem of multivariable digital model verification complexity can be effectively solved, and the confidence coefficient of complex system digital model verification is improved.

The invention discloses an anti-money laundering risk rule generation method and system based on a multi-modal large language model, and the method comprises the following steps: obtaining an initial training set composed of transaction data, adding a money laundering label based on a rule model, extracting transaction features through a multi-modal large language feature extraction model, and carrying out the expansion, forming a modeling training set by the initial training set and the expanded data set; fitting the multi-modal large language learning prediction model through the modeling training set to obtain a money laundering risk value of the transaction data, and calculating an actual contribution value of the structured features; and inputting the verification set into the multi-modal large language learning prediction model, verifying to obtain a money laundering label re-determination result, updating the money laundering label re-determination result to the initial training set, and then generating an updated rule model according to the verification result and the contribution value of the structured feature to the money laundering risk value. According to the invention, multi-mode transaction data can be integrated, a risk prediction model of money laundering transaction is formed, real-time adjustment and updating are carried out, and money laundering transaction behaviors can be accurately identified.

The invention provides an integrated circuit model verification method and system, computer equipment and a storage medium. The method comprises the steps that parameters in a model library are analyzed through a script, and model parameters needed by verification are automatically extracted; a standard configuration file format is obtained, the model parameters are loaded into a standard configuration file, and a simulation configuration file is generated; obtaining netlist file formats required by different simulators, and generating netlist files which comprise output instructions and are adaptive to different simulators in batches according to the simulation configuration file; calling each simulator to execute the corresponding netlist file for simulation, and outputting an intermediate file; analyzing the numerical data in the intermediate file, and generating a standard format file; and comparing the standard format files generated by different simulators to obtain a verification result. According to the scheme, batch parameter configuration, netlist generation, simulation execution and result processing of the integrated circuit model can be realized, so that the model verification efficiency is improved.

The invention discloses a compound structure identification database construction method, system and device and a medium, and the method comprises the steps: inputting a model training data set into a plurality of retention time prediction models for training, and obtaining a plurality of trained retention time prediction models; verifying the plurality of trained retention time prediction models by adopting a model verification data set, and selecting an optimal retention time prediction model; inputting the migration training data set and the migration verification data set into the optimal retention time prediction model for migration learning to obtain a target retention time prediction model; inputting to-be-predicted compound structure data into the target retention time prediction model for prediction to obtain predicted retention time; and constructing a compound structure identification database according to the predicted retention time. The data in the compound structure identification database can be perfected, and the accuracy of the compound structure identification database is improved, so that the database performance is improved.

Disclosed herein are systems and methods for cross-domain training of sensing-system-model instances. In an embodiment, a system receives, via a first application programming interface (API), an input-dataset selection identifying an input dataset, which includes a plurality of dataframes that are in a first dataframe format and that have annotations corresponding to one or more sensing tasks performed with respect to the dataframes. The system executes a plurality of dataframe-transformation functions to convert the plurality of dataframes of the input dataset into a predetermined dataframe format. The system trains an instance of a first machine-learning model using the converted dataframes of the input dataset to perform at least a subset of the one or more sensing tasks. The system outputs, via the first API, one or more model-validation metrics pertaining to the training of the instance of the first machine-learning model.

The invention belongs to the technical field of prognosis models, and particularly relates to a diffuse large B-cell lymphoma risk prognosis model based on lactic acid metabolism related genes. The model construction comprises the following steps: S1, data acquisition and processing; s2, constructing a prognosis model; s3, verifying and evaluating the prognosis model; and S4, application of a prognosis model. GSE10846 and GSE87371 data sets are used for screening lactic acid metabolism related genes (LMRGs), a prognosis risk model is constructed and verified, and association of the prognosis risk model with clinical pathological characteristics, immune characteristics and treatment response is disclosed.

The invention discloses a register model and register transfer level generation and test method, device, equipment and medium, and relates to the technical field of integrated circuit design verification, and the method comprises the following steps: analyzing a comma separation value table file by using a register generation platform to generate a register model, a register transfer level and a register test vector; the comma separation value table file is a file used for describing register information; generating a general verification methodology environment by using a test environment generation platform, and integrating the general verification methodology environment based on the register model, the register transfer level and the register test vector to obtain a corresponding integrated test environment; and compiling the integrated test environment, if the compiling is successful, running the register test vector in the integrated test environment which is successfully compiled, and outputting a corresponding test result. Therefore, the automation degree of register model verification can be improved, and the verification efficiency of front-end verification is improved.

The systems and methods disclosed herein relate to a model validation platform that enables dynamic validation of a user's prompt for a large language model (LLM) in order to evaluate the validity of the prompt and the suitability of a large language model for processing the prompt. For example, the platform enables an estimation of the resource allocation associated with processing the prompt with a given LLM, as well as a modification of the prompt, prior to the processing the prompt with the selected LLM. The platform can further validate the output prior to transmitting the output to a server system for display to the user. By doing so, the platform enables dynamic evaluation of a request to execute an LLM, as well as evaluation of resulting outputs, for accuracy and efficiency improvements in data processing or software development pipelines.

The invention discloses a target detection online learning dynamic sample selection method and system, computer equipment and a storage medium. The method comprises the steps that classification uncertainty and positioning uncertainty of samples to be screened are obtained through Monte Carlo Dropout sampling; constructing multi-dimensional feature vectors including classification uncertainty, positioning uncertainty, knowledge gap matching degree and the like; constructing a dynamic weight learning network based on an attention mechanism, and calculating the weight of each feature in combination with a model verification set performance index; and performing multi-dimensional value scoring on the samples according to the feature weights, and screening out an optimal sample subset in combination with calculation power limitation so as to complete online updating of the model. According to the method, the sample value is comprehensively evaluated through multi-dimensional feature fusion, different scene requirements are adapted by utilizing dynamic weights, and resource consumption and updating effects are balanced in combination with computing power perception sampling, so that the adaptability and detection precision of the model in a dynamic scene are effectively improved, and meanwhile, the dependence on manual annotation is reduced.

The invention discloses a fully-weathered argillaceous siltstone elastic-plastic damage constitutive model establishing system, and relates to the technical field of elastic-plastic damage constitutive models.The system comprises a parameter collecting and labeling module used for obtaining a data set and conducting classification labeling and test condition information; the parameter processing and matching module is used for preprocessing the parameters to extract a feature parameter set and performing information association to supplement boundary condition data; the damage elastic-plastic feature module is used for presetting a database and a weight distribution rule and performing weighted fusion to form a damage elastic-plastic coupling feature parameter set; the constitutive model construction module is used for constructing a constitutive model, performing iterative training on the constitutive model and calculating damage parameters and a prediction result; and the model verification report module is used for verifying the damage parameters and the prediction result and judging the effectiveness of the constitutive model according to the verification result. According to the method, the multi-dimensional core characteristic parameter set is constructed by obtaining mechanical property test parameters and the like, and the limitation of single test data is broken through.

The invention relates to the technical field of rail transit construction, and discloses an analogue simulation method for a shield mud film gas check test, which comprises the following steps: S1, establishing an analogue simulation system for the shield mud film gas check test; s2, preparing slurry according to requirements; s3, measuring the porosity, density, residual moisture content, water conductivity coefficient, elastic modulus, Poisson's ratio and water holding capacity parameters of the slurry and the stratum soil body; s4, all the material parameters obtained in the S3 are substituted into an analogue simulation system of the shield mud film air closing test, and then a small amount of mud film air closing tests are used for carrying out model verification; and S5, carrying out simulation calculation analysis. According to the method, an indoor gas closing test simulation model is established on the basis of a Biot pore elasticity theory under a fluid-solid coupling condition, different water holding capacity characteristics of a mud film and a stratum are considered, and model verification is carried out through a gas migration rule and leakage flow evolution data obtained through an indoor test; and the rapid evaluation of the closeness characteristic of the mud film under complex geological conditions and shield parameter working conditions is realized.

The invention discloses a molten steel segregation prediction method and related equipment, and relates to the technical field of continuous casting, and the method comprises the following steps: obtaining process parameter data of electromagnetic stirring in a continuous casting process and corresponding molten steel segregation data; preprocessing the process parameter data and the molten steel segregation data to obtain a training data set for model training and a test data set for model verification; adopting a grey wolf algorithm to optimize hyper-parameters of the random forest model to obtain an optimized random forest model; training the optimized random forest model by using the training data set, and verifying the prediction performance of the optimized random forest model through the test data set; on the basis of the trained and verified optimized random forest model, real-time process parameter data are input, and a prediction result of the molten steel segregation degree is output; and optimizing and adjusting process parameter data of electromagnetic stirring according to the prediction result.

The invention discloses a crystal size distribution soft measurement method integrated with mechanism constraints. The method comprises the following steps: 1) data acquisition and integration; 2) modeling training and model testing: constructing a crystal size distribution soft measurement model integrated with mechanism constraint, training a training data set, injecting crystallization process dynamics knowledge into the Kolmogorov-Arnod network in a loss function form, improving the nonlinear modeling capability and credibility of the model, and performing model testing on the Kolmogorov-Arnod network; and verifying test set data by using the trained model. According to the method, the knowledge of the crystallization dynamic process is injected into the Kolmogorov-Arnod network in the form of the loss function, so that the prediction capability of the model on the crystal size distribution in the crystallization process and the credibility of the model are effectively improved.

The invention provides a karst underground water level prediction method based on a PSA-CNN-LSTM-Attention deep learning algorithm. The karst underground water level prediction method based on the PSA-CNN-LSTM-Attention deep learning algorithm comprises the steps that 1, data and input indexes are obtained; 2, data processing; 3, model selection and construction; and 4, verifying the model. According to the method, a large amount of underground water level data is processed and analyzed by comprehensively applying a convolutional neural network (CNN), a long short-term memory (LSTM) network, an attention mechanism and a PID search optimization algorithm (PSA). A high-precision underground water level prediction model is constructed by coupling a convolutional neural network (CNN) and a long short-term memory (LSTM) network technology and integrating a global average pooling layer and an SE attention mechanism, and the method is particularly suitable for dynamic change prediction of the underground water level in a karst area. Besides, model parameters are adjusted by adopting a PSA global optimization algorithm to ensure that the model can exert optimal performance under different environments and data conditions, so that not only is the adaptability of the model enhanced, but also the prediction accuracy of the model under a complex geological background is improved.

The invention discloses a cross-scene wastewater treatment control system based on knowledge distillation and rule migration and an implementation method, and relates to intelligent control of industrial wastewater treatment. The method comprises the following steps: collecting operation parameters, control rules and wastewater treatment effects of a plurality of wastewater treatment systems, and completing data preprocessing; then performing feature extraction on the wastewater treatment process and the control rule by utilizing a knowledge distillation technology, and constructing a standardized multi-system data set; constructing a knowledge distillation base model for data fusion of different processing systems, and performing model verification; constructing an interpretable general rule set, namely calculating a feature matching degree of the general rule set in a new scene, and performing rule migration correction by utilizing a specific small sample; and substituting the generated target system rule into a triple discriminator for verification, and determining a final deployable wastewater treatment system control rule. According to the method, a machine learning model driven by multi-source data is converted into a general interpretable rule base through a regular distillation algorithm, and intelligent control over lossless industrial deployment is achieved.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

The invention discloses a model robustness automatic evaluation method based on large model driving and related equipment. The method comprises the following steps: inputting a task template of a tested model into a dynamic Prompt engine module to obtain a sample generation instruction; generating an adversarial sample according to the sample generation instruction through a sample generation and verification module, and performing model evaluation on the adversarial sample; if the confrontation sample passes the model verification, extracting a high-dimensional multi-modal feedback vector of the tested model through a multi-modal feedback analysis module; a reusable semantic instruction is generated through a strategy control module according to the high-dimensional multi-modal feedback vector so as to optimize a generation strategy; and if the confrontation sample does not pass the model verification, performing effect evaluation on the confrontation sample through an evaluation and interpretation generation module and generating an interpretability evaluation report. According to the method, progressive generation of the adversarial sample and exponential-level improvement of the model robustness evaluation efficiency can be realized, a clear model improvement direction is provided for developers, and the method can be widely applied to the technical field of computers.

The invention relates to the technical field of electric power simulation, and discloses a hardware-in-the-loop simulation-oriented new energy equipment model verification method and system, and the method comprises the steps: constructing a new energy equipment basic model based on a physical equation, carrying out the compensation of a dynamic working condition error through combining with an LSTM neural network, and generating a hybrid parameterized model; fitting voltage and current data through a particle swarm optimization algorithm, and dynamically adjusting impedance parameters to complete model calibration; a time domain step response test, frequency domain characteristic analysis and a voltage sag fault experiment are executed on a hardware-in-loop platform, and the performance of the model is verified in a multi-dimensional mode; and generating a standardized verification report containing a steady-state error and a simulation step length index based on the deviation between the dynamic response curve and the measured data. According to the method, a mechanism model and a data driving technology are fused, the problems that dynamic characteristic adaptation is insufficient and parameter calibration depends on experience in traditional verification are solved, and the simulation precision and verification efficiency of the model under complex working conditions are improved.

Conventional estimation model generating apparatuses are required to be able to estimate information on a flow rate of a target river. An estimation model generating apparatus 101 includes: a basin data acquiring unit 142 that acquires basin data of a basin of a river of interest from an image containing the basin; a candidate model acquiring unit 143 that acquires one or more candidate models for estimating information on a flow rate of the river based on the basin data; a verifying unit 145 that verifies validity of at least one candidate model out of the one or more candidate models using verification data; an estimation model acquiring unit 147 that acquires one candidate model out of the one or more candidate models as an estimation model based on a verification result; and a model accumulating unit 149 that accumulates the acquired estimation mode. Accordingly, it is possible to estimate information on a flow rate of a target river.