200 results about "Model validation" patented technology

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.

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 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 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.

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.

The invention relates to the field of power system modeling and parameter identification, and provides a synchronous generator parameter identification method based on an improved tuna swarm optimization algorithm. Dividing the full parameter set of the to-be-identified synchronous generator into a d-axis parameter subset, a q-axis parameter subset and a mechanical parameter subset according to the internal physical association of the to-be-identified synchronous generator; performing multiple improvements on the tuna swarm algorithm to obtain an improved tuna swarm optimization algorithm; integrating the to-be-identified parameters in the d-axis parameter subset and the q-axis parameter subset into a high-dimensional parameter vector, constructing a target function based on the comprehensive test data, and performing multi-parameter synchronous global identification based on the target function by adopting an improved tuna swarm optimization algorithm to obtain an identification result; and carrying out model verification and validity analysis on the identification result. According to the method, the problem of parameter correlation distortion of traditional step-by-step identification is solved, the global optimization ability and convergence efficiency of the algorithm are improved, and the method can adapt to the accurate identification requirement of the high-dimensional strong coupling parameters of the synchronous generator.

The invention relates to the technical field of environmental risk management and control, in particular to a tire wear particle environment fate prediction model construction method based on multi-dimensional data. Comprising the following steps: data acquisition and preprocessing; a feature importance analysis model based on a random forest is constructed, key factor mining is performed on the preprocessed collected data, core factors influencing tire wear particle environment fate are screened by calculating Gini importance values of the key factors, and feature vectors and confidence coefficients of the core factors are output; classifying and integrating factors; carrying out model association construction; model training storage; and performing model verification feedback. According to the method, the continuity, timeliness and consistency of tire wear particle sample data and environment associated data are improved through the built tire wear particle environment fate prediction model and through cooperative collection, space-time registration and noise filtering preprocessing of the multi-source monitoring equipment, and a high-quality data basis is provided for subsequent model building.

The invention discloses a bulk cargo ship navigational speed prediction system based on feature engineering and an integrated tree model, and relates to the technical field of navigational speed prediction. The bulk cargo ship navigational speed prediction system based on the feature engineering and the integrated tree model comprises a feature optimization judgment and model training module, a model optimization judgment module, a model structure optimization judgment module and a navigational speed prediction output module. According to the method, whether bulk cargo ship characteristic optimization is carried out or not is judged through bulk cargo ship state characteristics, the optimized bulk cargo ship characteristics are input into a bulk cargo ship navigational speed prediction model for training, and then whether model parameter optimization is carried out or not is judged based on a model verification result; judging whether to perform model structure optimization or not based on a model verification result after model parameter optimization, and finally inputting real-time bulk cargo ship characteristics into the bulk cargo ship navigational speed prediction model after model parameter optimization to obtain the bulk cargo ship navigational speed, thereby improving the effectiveness of the bulk cargo ship navigational speed prediction. The problem of low effectiveness of bulk cargo ship navigational speed prediction in the prior art is solved.

A method may include receiving, by a first user device from a network node or a second user device, 1) a positioning measurement report including at least one positioning measurement measured by a reference device, and 2) reference positioning-related information to be used for testing and / or validating a machine learning model; determining estimated positioning-related information as outputs of the machine learning model based on at least a portion of the positioning measurement report as inputs to the machine learning model; determining a performance indication of the machine learning model based on the reference positioning-related information and the estimated positioning-related information, wherein the performance indication indicates a performance or accuracy of the machine learning model; and performing, by the first user device, an action based on the performance indication.

The invention relates to the technical field of digital twinning, in particular to a digital twinning driven equipment information model construction system. Comprising a data acquisition and preprocessing unit; a digital twin modeling unit; the model verification and optimization unit adopts a virtual and real data real-time comparison mechanism and is combined with a self-adaptive optimization algorithm driven by deviation characteristics; and a model application and updating unit. A dynamic weight distribution algorithm is adopted in a model verification and optimization unit, the initial weight is adjusted based on the current load rate and the accumulative operation duration of equipment, and instantaneous fluctuation and trend deviation are distinguished through a two-dimensional fusion judgment algorithm in combination with the deviation duration and the trend slope. The problem of insufficient accuracy of virtual and real data comparison in the prior art is solved; according to different deviation properties, an adaptive genetic algorithm is adopted to generate an optimization strategy, and it is guaranteed that the digital twin equipment information model is consistent with the physical characteristics of an equipment entity for a long time.

The invention relates to the technical field of computational fluid mechanics and biological microfluidic design, in particular to a model order reduction method for rapid optimization of a circulating tumor cell (CTC) sorting structure, which comprises the following steps of: constructing a three-dimensional full-order computational fluid mechanics model by collecting channel geometric structure parameters and fluid working condition parameters, and generating a training data set; organizing a flow field snapshot into a matrix form, extracting a dominant mode, constructing a low-dimensional modal space by taking the dominant mode as a base vector, establishing a low-dimensional ordinary differential model through Galerkin projection, and training a parameter-modal coefficient mapping relation by adopting a deep neural network to form a complete reduced-order model; and finally, constructing a multi-objective optimization problem based on the reduced-order model, carrying out optimization iteration by adopting an evolutionary algorithm, and returning an optimization result to the full-order model for verification, so that rapid optimization design of the CTC sorting structure is realized, and an effective solution is provided for intelligent design of a biomedical microfluidic device.

The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to a track tracking control method for a tandem dual-rotor unmanned helicopter, which comprehensively considers factors such as aerodynamic force and inertia force and is completed through structural parameter measurement, aerodynamic force analysis, kinetic equation derivation and model verification and correction. Designing a self-adaptive sliding mode controller, defining a sliding mode surface, designing a control law and a self-adaptive law, ensuring the robustness of the system, and optimizing a control strategy; in combination with model prediction control, establishing a prediction model, defining a target function, improving the track tracking precision through rolling optimization and feedback correction, and correcting prediction errors and interference influences through sliding mode control; a multi-task switching control strategy is designed, task stages are divided, parameters are set, switching conditions are determined, and a fuzzy logic reasoning or neural network algorithm is adopted to realize rapid adjustment of the control strategy. The technology can accurately track the track, enhance the robustness, improve the real-time performance, flexibly adapt to multiple tasks and have a good application prospect.

The invention relates to a multi-body separation modeling method based on wind tunnel model release test data. The method comprises the following steps: 1, preparing and preprocessing test data; 2, performing polynomial fitting on the output time sequence data; step 3, fitting precision analysis; 4, input parameters and polynomial coefficients are subjected to normalization processing; 5, analyzing the correlation between input and output parameters; step 6, establishing a regression prediction model; and 7, verifying the model. When the method is used for processing the test data of the wind tunnel launching model, polynomial fitting is carried out on the trajectory and attitude time sequence data, a multi-body separation prediction model is established directly based on a small amount of wind tunnel model launching test data, and the cost-reducing and efficiency-increasing requirements of modern aerospace engineering are met.

The invention discloses a dual reasoning acceleration method based on draft prediction and compressed memory, which comprises the following steps of: 1) acquiring an input context of model reasoning and a token sequence generated by a model at each stage of reasoning; 2) according to an input context of model reasoning and a token sequence generated by the model, predicting subsequent candidate tokens in parallel as draft tokens; the method comprises the following steps of (1) performing parallel generation, (2) performing main model verification, (3) organizing draft tokens according to a tree structure, (4) performing main model verification, (5) compressing each reasoning block into compressed tokens with a fixed length s, and (6) performing context reconstruction. According to the method, through the combination of parallel generation and context compression, the problem of serial low efficiency of traditional autoregression is solved, and the redundant overhead of long-sequence reasoning is relieved.

The present invention provides a model for preoperative verification of a dental treatment plan, a verification tool, a method for manufacturing a verification model, a verification system, and a verification program. The model of the present invention has a model body, a hole specification portion, and a target model portion. A space is formed between the hole specification portion and the target model portion, and the space enables visual confirmation of a range in which a front end of a surgical tool can reach from the hole specification portion toward the target model portion, and enables visual confirmation of a range in which the front end of the surgical tool can reach from the hole specification portion in a tooth arrangement direction and a direction intersecting the tooth arrangement direction.

The invention relates to the technical field of seismic facies intelligent identification models, in particular to a seismic facies intelligent identification model construction method and system and a storage medium. The method comprises the following steps: acquiring three-dimensional seismic data and seismic facies label data corresponding to the three-dimensional seismic data, and performing data preprocessing to generate a standard training data set; determining network structure parameters and generating a network model initial architecture based on the structural features of the standard training data set; according to the initial architecture of the network model and the standard training data set, carrying out network parameter training and generating multiple groups of candidate model parameters; through a preset model verification evaluation process, screening out an optimal model parameter from the multiple groups of candidate model parameters, and constructing a seismic facies intelligent identification model; according to the method, the seismic facies intelligent identification model is constructed, so that standardization and automation of the whole process from data preparation to model optimization are realized, and the accuracy and reliability of seismic facies identification are remarkably improved.

The invention relates to the technical field of new energy power generation and power grid modeling, in particular to a multi-working-condition matching model parameter identification method for new energy actual measurement modeling. According to the technical scheme, the multi-working-condition matching model parameter identification method for new energy actual measurement modeling comprises the following steps that actual measurement data of a new energy converter under multiple fault working conditions are obtained, and a fault parameter matrix containing current control values during symmetric faults and asymmetric faults is constructed; calculating a current limiting value parameter based on the fault parameter matrix; an initial identification matrix is constructed, an intermediate matrix is generated according to the current limiting value parameters, and the intermediate matrix is used for marking elements reflecting real control characteristics in the identification matrix. Multi-working-condition parameter intelligent optimization and model verification are achieved, repeated simulation and comparison calculation of a large number of working conditions are avoided, and a modeling scene with the large number of working conditions, the large deviation item calculation amount and the complex model parameters can be efficiently completed.

The application discloses a kind of methods for evaluating the delamination fracture performance of wind power blade sandwich structure, it is related to blade sandwich structure experiment and simulation field, S1: prefabricated crack sample preparation;S2: sample processing;S3: experimental test;S4: theoretical analysis;S5: simulation modeling;S6: simulation model verification;S7: interface delamination backtracking analysis;The application can accurately obtain the delamination fracture material parameters of wind power blade sandwich structure, analyze the delamination failure load of sandwich structure by simulation model, obtain interface damage, crack propagation and other information of sandwich structure, and quantitatively evaluate the resistance of blade sandwich structure to delamination crack propagation.

The invention relates to the technical field of intelligent medicine, and discloses a microorganism and environmental factor association modeling method and system.The method comprises the steps that according to distribution data of features in a standardized data set, information entropy values of the features are determined; screening out features of which the feature weights are higher than a preset threshold in the feature entropy set to obtain a key feature set; taking the key feature set as a node, taking the interaction relationship as an edge, and taking the association strength as an edge weight to obtain an association model of the microorganisms and the environmental factors; performing performance evaluation on the association model by using the independent verification data set to obtain a model verification report of the association model; inputting the standardized data set of the target microorganisms and the target environmental factors, and obtaining an association network diagram of the target microorganisms and the target environmental factors, wherein the model verification report is a passed association model; according to the method, the accuracy of correlation modeling of microorganisms and environmental factors can be improved.

The application provides a large language model reasoning method and device based on a diffusion model, a terminal and a medium. The application generates a draft sequence by using a draft model based on a diffusion model framework, and verifies the draft sequence by using a large language model. The application takes advantage of the characteristic that the diffusion model naturally supports parallel processing, so that the length of the draft sequence is significantly increased, thereby reducing the number of large model verifications, improving the reasoning efficiency of the large language model, and improving the utilization efficiency of computing resources.

The invention relates to an equipment monitoring model training method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining first monitoring data used for model training, second monitoring data used for model testing and third monitoring data used for model verification of multiple pieces of to-be-monitored target equipment, according to the first monitoring data corresponding to each target device, performing model training on the to-be-trained model to obtain a plurality of device monitoring candidate models, according to the second monitoring data and the third monitoring data, performing data verification on each device monitoring candidate model, and performing data verification on the to-be-trained model from the plurality of device monitoring candidate models. And determining a plurality of equipment monitoring sub-models according to the third monitoring data, and determining a target equipment monitoring sub-model corresponding to each target equipment from the plurality of equipment monitoring sub-models according to the third monitoring data to obtain a target equipment monitoring model for monitoring and early warning the specified operation index data of the target equipment.

A method of wireless communication by a first network device includes receiving a machine learning model, the machine learning model being unvalidated and trained. The method also includes quantizing and compiling the machine learning model. The method further includes obtaining verification data. The method still further includes validating the machine learning model with the verification data to determine a performance level of the machine learning model. A method of wireless communication by a first network device includes transmitting machine learning model verification data to a second network device. The method also includes receiving a report, from the second network device, indicating performance of a machine learning model with the verification data. The method further includes validating the machine learning model in response to the performance satisfying a performance condition.

The present application relates to the technical field of knowledge tracking, and relates to a method for dynamically evaluating student knowledge level based on a double attention mechanism, comprising the following steps: S1. obtaining interactive information of a student learning process, and grouping the interactive information into a sequence; S2. dividing the interactive sequence into three parts, namely a dynamic problem level sequence, an average skill level sequence and an additional feature sequence; S3. inputting different sequences into corresponding modules for training, and obtaining a student knowledge state through a long short-term memory network and multiple attention mechanisms; S4. inputting the knowledge state into an interpretability module to evaluate the knowledge level; and S5. recording training model evaluation indexes, verifying the interactive sequence of the student through a model updated by parameters, and evaluating the knowledge level of the student. The present application fully mines the interactive information of the student, evaluates the knowledge state of the student from different angles, and improves the accuracy of predicting the future performance of the student.

The invention discloses a novel equivalence device structure closing and single-step repairing method and system in medical diagnosis, drug discovery and physiological status modeling, belongs to the field of medical diagnosis analysis, biological information processing and model verification, and ensures the reliability and traceability of the process through structural equivalence device detection and receipt track recording. The method monitors sequential equivalents (ABBA rectangles for checking consistency of operational orders) and conservation equivalents (triangles for checking information conservation and energy balance) during operation to find anomalies in diagnostic inference chains, drug action pathways, or physiological closed loops (such as predictor deviations, energy conservation imbalances, or model inconsistencies). When a non-equivalence condition is detected, the system selects a unique repair adapter from a geometric layer, a frame layer and a field layer for correction according to a preset priority, and the deviation metric mu is strictly reduced by one (delta mu = 1) during each repair, and is gradually converged to an equivalence closed state.

The present application relates to a kind of multilayer structure reference body for infrared heat conduction model verification, it is related to the field of infrared model calibration verification, including aluminium plate, PVC plate, heating film and thermal barrier layer glued in turn, several sensors are pasted with spacing on the outer surface of aluminium plate by high-temperature-resistant adhesive tape, sensor is inserted between aluminium plate and PVC plate, and between PVC plate and heating film, sensor is thermosensitive sensor, sensor is connected to aviation plug by grouping threading and is electrically connected with control box integrated with sensor controller, heating film controller and power meter, the present application is packaged by the design of the temperature sensor of each layer material regionally laid out, power meter and heating film connection, with the advantages that heating power in test is recorded in real time and the surface temperature of each layer material, provide clear input condition for simulation and accurate model verification data.

Embodiments of the present disclosure provide a solution for model management. In a solution, a first device transmits, to a second device, capability-related information comprising a set of functionalities supported by the first device; performs at least one model-related procedure comprising at least one of the following: a model identification, a model transfer, a model training, a model validation, or a performance monitoring of a model; and determines, from the set of functionalities, at least one applicable functionality after completion of performing the at least one model-related procedure.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.