330 results about "Performance prediction" patented technology

The application belongs to the technical field of material performance prediction and uncertainty analysis, and proposes a beryllium aluminum alloy performance uncertainty quantification method based on Bayesian ensemble learning, which is innovative in constructing and training multiple independent performance prediction models to form the basis of ensemble learning. After the training of each model is completed, a performance prediction value can be output for a new input sample. After obtaining the prediction outputs of multiple independent models, a Bayesian fusion method is used to comprehensively process the prediction results on the probability level to obtain the fused performance prediction distribution; and the complete results of the beryllium aluminum alloy performance prediction are output in a clear, intuitive and convenient engineering application format. The application has the advantages that the robustness and generalization ability of the prediction results are improved, a decision basis is provided for material performance evaluation, and good adaptability is achieved for the case of limited data quantity, and important innovations are achieved in the aspects of material performance uncertainty quantification theory and engineering application.

This invention relates to the field of engineering structure performance prediction and optimization technology, specifically disclosing an engineering full life cycle performance prediction and optimization system. The system collects time-series data of load, acceleration, and strain of engineering structures under extreme loads; constructs a physical consistency mapping operator based on real-time input data and finite element prior information, outputting a predicted response field; performs intrinsic orthogonal decomposition on the finite element prior information to extract the basis vector and integral point subset, trains the time-series evolution rule of the basis coefficients, and outputs low-dimensional basis coefficient prediction values; compares the real-time input data with the predicted values, and triggers online basis assimilation update when the deviation exceeds a threshold, assimilating the low-dimensional basis coefficients to output a corrected stiffness field; substitutes the corrected stiffness field into the time-series evolution rule to extrapolate the future response field, generating a smooth control force command for the active mass damper; this invention achieves real-time closed-loop optimization control of engineering structures under extreme loads.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

This invention discloses a method for predicting concrete performance based on multivariate nonlinear regression analysis, comprising the following steps: obtaining a real-valued dataset of concrete performance under different combinations of conditional parameters using an orthogonal strategy; defining the type of regression equation and solving the parameters in the regression model using the least squares method based on the aforementioned dataset; verifying the accuracy of the regression model using the aforementioned dataset and additional datasets; and predicting the concrete performance of different parameters using the validated regression model. The results show that this performance prediction method can establish an excellent regression prediction model based on a small amount of real data and achieve rapid and accurate prediction of concrete performance with different parameters.

This invention relates to a physical information neural network-based intelligent performance prediction method and system for automated multi-material fiber placement on complex curved surfaces in aircraft. The invention includes: collecting fiber placement process parameters; performing standardized preprocessing and physical constraint-enhanced sampling to obtain an enhanced training dataset; constructing a high-order nonlinear sparse regression candidate library based on the enhanced training dataset; jointly identifying piecewise smooth physical differential equation systems and their mode switching logic using sparse Bayesian regression and Hidden Markov Models to obtain an embeddable inverse mechanism model; constructing a Physical Information Embedded Generative Adversarial Network (PI-GAN); collaboratively optimizing the generator and discriminator through adversarial training to output surface defect indices and mechanical performance indices; and initiating incremental self-learning when introducing new process scenarios to achieve model self-evolution. This invention achieves end-to-end accurate prediction of multi-dimensional quality indices such as surface defects and mechanical performance.

A method for analyzing data center congestion control includes capturing packets traversing network switches in a data center configured with data center quantized congestion notification (DCQCN) parameters to regulate congestion in the data center. The method further includes generating, from the captured packets, a congestion notification packet (CNP) profile for the data center and a visualization of real traffic rates in the data center. The method further includes configuring a DCQCN performance predictor with the DCQCN parameters and providing the CNP profile as input to the DCQCN performance predictor. The method further includes simulating, by the DCQCN performance predictor, congestion control of the data center and generating, as output, a visualization of simulated ideal traffic rates in the data center given the CNP profile and the DCQCN parameters.

The application discloses a mirror shape preserving optical bench structure cross-scale optimization design method and device, and relates to the technical field of optical engineering. The method comprises the following steps: in the offline stage, parameterized point array modeling and equivalent performance prediction are carried out, and a prediction model is established. In the online stage, the optical bench structure is selected, the design domain is determined and the finite element is discretized, then the entity and point array areas are divided, the load and boundary conditions are applied, and the design variables are defined. The prediction model is called to obtain the point array mechanical performance to calculate the structure strain energy, the mirror node displacement is extracted, and the mirror surface shape information is obtained by fitting. An optimization model is established with the minimum structure strain energy as the objective function and the mirror surface shape information as the constraint, and optimization iteration is carried out with the aid of sensitivity information. If the optimization model does not converge, the design variables are adjusted, the constraints are redefined, and the iteration is continued. If the optimization model converges, the optical bench cross-scale shape preserving optimization result is obtained. The method solves the problem that the traditional design method cannot meet the demand of a new generation of optical mechanical system in terms of light weight, stiffness and mirror shape preservation.

The application relates to a high-entropy alloy component design method and system covering the whole element periodic table, and the method comprises the following steps: collecting general material database resources in advance, extracting the core features and crystal graphs of the materials, and training a pre-trained large model; collecting and obtaining performance data of multi-element alloys, and constructing a probe test set; the probe test set is divided into multiple subspaces according to element types and phase structures; the pre-trained large model is used for alloy performance prediction in each subspace, and high-performance subspaces are screened out according to the prediction results; an optimization algorithm is used for alloy component optimization in each high-performance subspace; the restrictions on raw material elements and target alloy characteristics are added to ensure the solid solution of the target alloy; the verification is carried out through a theoretical calculation and an experimental verification method, and if the verification is passed, the high-entropy alloy component is finally obtained. Compared with the prior art, the application has the advantages of reducing the data cost of high-entropy alloy component design, being accurate and being able to effectively guide experiments.

The application discloses a small-sample steel quality multi-index joint prediction method and device and a medium, relates to the field of material performance prediction, and comprises the following steps: determining a standardized data set comprising steel chemical composition, process parameter characteristics and multi-dimensional mechanical performance index data; configuring a target order of the multi-dimensional mechanical performance index; constructing a regression chain model based on the target order; in the regression chain model, the prediction value of a previous mechanical performance index is used as an additional input feature of a corresponding regressor of a subsequent mechanical performance index; adopting a nested cross-validation strategy to perform hyperparameter search and optimization on the regression chain model, and determining a target hyperparameter combination; performing full-amount retraining on the regression chain model based on the target hyperparameter combination, and obtaining a trained steel performance joint prediction model; and inputting the chemical composition and production process parameters of the steel to be predicted into the steel performance joint prediction model, and obtaining a joint prediction result of each steel performance index corresponding to the steel to be predicted.

This invention provides an intelligent experimental decision-making method and device for foamed lightweight soil mix proportions, relating to the field of building material design technology. The method includes: acquiring experimental data of multi-source foamed lightweight soil and preprocessing it to obtain a standardized tabular dataset; using the standardized tabular dataset as context input to a pre-trained tabular prior data fitting network model to obtain performance prediction values; employing the SHAP value analysis method to calculate the contribution value of input features to the performance prediction values, thus obtaining the contribution relationship between input features and performance prediction values; using the tabular prior data fitting network model as the performance prediction model, establishing a multi-objective optimization model by combining material cost and material density, and setting constraints based on the contribution relationship; and using a non-dominated sorting genetic algorithm to solve the multi-objective optimization model, searching for a set of Pareto optimal mix proportion schemes. This invention can automatically search for Pareto optimal mix proportion schemes, improving the efficiency of mix proportion design.

This invention discloses an intelligent optimization method for communication chip design parameters based on deep learning, comprising: S1, constructing chip topology graph data and encoding a graph structure input using Kirchhoff's laws; S2, collecting historical data to construct a standardized training sample library; S3, aggregating node information to learn parameter coupling relationships and outputting a performance prediction model; S4, introducing an improved QPSO algorithm to construct a physically constrained pseudo-gradient guiding term, using the first derivative to force particle movement in flat regions, and outputting a potential global optimal solution region; S5, using the MOEA / D algorithm to decompose a multi-objective problem and iteratively selecting non-dominated solution sets as candidate sets in parallel; S6, backfeeding a high-precision simulator for verification and incremental updates, outputting the optimal parameter combination. This invention achieves physical constraint modeling and gradient-guided optimization of design parameters, effectively improving optimization efficiency, prediction accuracy, and design feasibility.

This invention relates to a six-DOF microgravity quality assessment method based on mission phase-driven approaches, belonging to the field of spacecraft control and ground simulation. It constructs a multi-physics fusion six-DOF microgravity quality index, divides the on-orbit service mission phase using a finite state machine and matches dynamic weights and assessment thresholds, combines digital twins to achieve virtual-real consistency verification, employs rapid independent component analysis and extended state observers to complete disturbance decoupling and contribution rate quantification, and uses a graph neural network long short-term memory network model and Shapley sum interpretation algorithm to achieve pre-experiment performance prediction and in-experiment anomaly diagnosis. This invention addresses the technical problems of existing microgravity ground simulation assessments, such as the disconnect between static thresholds and on-orbit mission requirements, the inability of a single index to cover multi-DOF coupling characteristics, the lack of virtual-real consistency verification, the inability to quantify and attribute disturbance sources, over-reliance on expert experience, and post-event offline assessment.

The application discloses a MOFs synthesis route multi-objective optimization method based on machine learning. The method is to collect the synthesis conditions of prepared Ce-UiO-66, and evaluate the defect content and thermal stability thereof through a thermogravimetric analysis curve, as initial data set; the data set is randomly divided into a training set and a test set, eight algorithms are adopted to model each performance of Ce-UiO-66 and select the proxy model for performance prediction; the target achievement probability (PA) value of each performance in the synthesis space is calculated and expanded into a multi-objective evaluation factor; the Ce-UiO-66 material is prepared; the obtained material is subjected to characterization test, if the test data does not meet the requirement, the data set and the proxy model are updated. The application has the advantages of low cost, short cycle and the like in optimizing the catalytic and stable performances of MOFs based on reliable experimental data and machine learning, and can be popularized to the design of synthesis routes of other materials.

The application provides a floating floor slab impact sound insulation performance optimization method, device, equipment and storage medium, keyword extraction is performed on target building space information input by a user through a natural language processing module, semantic similarity matching is performed in a pre-constructed noise source library, a target noise source spectrum corresponding to the target building space and a sound insulation standard limit value are automatically obtained, then a floating floor slab sound insulation performance target is determined based on spectrum analysis, subsequently, floating plate material parameters and elastic cushion layer parameters are substituted into a floating floor slab sound insulation performance prediction model established based on a finite element method, impact sound insulation performance prediction values under candidate material combinations are quantitatively calculated, finally, a multi-objective optimization model is constructed and solved in combination with user design constraint conditions, and an optimal design scheme of the floating floor slab matched with actual requirements of the target building space is output, so that precise matching and global optimal design of the acoustic requirements of the building space and the construction scheme of the floating floor slab are realized.

The invention discloses a tar absorbent performance prediction and high-throughput screening method, system and equipment and a medium. The method comprises the following steps: constructing an absorption assistant structure library and a tar component structure library; respectively sampling the two structure libraries, establishing a water phase-absorption aid-oil phase model, and carrying out sufficient molecular dynamics simulation until a data set with a specified scale is formed; pre-training the deep learning network model; selecting an actual tar component, determining a tar component descriptor, and screening out an absorption assistant subset by using a pre-trained deep learning network model; the method comprises the following steps: determining absorption aids of a plurality of representative structures in an absorption aid subset, determining absorption rates through experiments, performing fine tuning training on a model to obtain a high-throughput screening model, and predicting all structures in an absorption aid structure library to obtain the absorption aid with the highest absorption rate. The method has the advantages of high-precision prediction, low experiment cost, high expandability and the like, and is suitable for rapid research, development and screening of the absorption additive material.

The application discloses a concrete performance prediction method based on an orthogonal strategy and machine learning. The method adopts the orthogonal strategy to obtain real labeled data of concrete performance under different parameter combinations, forms a training data set, and based on the training data set, comprehensively trains and optimizes multiple machine learning models, and evaluates the model prediction performance through an additional test set to build an optimal machine learning model. The final machine learning model is used to realize the concrete performance prediction under any parameter combination within a condition range. The results show that the performance prediction method can establish a performance prediction model with excellent accuracy through a small amount of real data, and can realize high-throughput prediction of concrete performance under different parameter combinations, and has a broad application prospect.

The application discloses a kind of variable scale performance prediction methods of fuel cell system combined with neural network, construct the proton exchange membrane fuel cell model combined with neural network algorithm, realize the variable scale performance prediction of proton exchange membrane fuel cell system, and the proton exchange membrane fuel cell model includes air supply module, hydrogen supply module, water-thermal management module, battery stack module and neural network fitting module;Actual output power of fuel cell system is calculated;After all module combination connection, it is constituted into a variable scale numerical simulation model of proton exchange membrane fuel cell system combined with neural network algorithm.The application combines the advantages of neural network and mathematical modeling, uses limited amount of data resources, computer resources and time cost, completes high-precision simulation modeling of proton exchange membrane fuel cell, to provide guidance for the application of proton exchange membrane fuel cell in aviation field.

The application discloses a kind of atomizer atomization numerical simulation methods based on temperature change characteristics, belong to atomizer performance prediction technical field.The method constructs simulation dataset, and sequentially executes temperature field solution, property update, droplet formation solution, transport solution, blockage risk integral calculation and constraint correction;Through multilevel transient coupling algorithm, layered transient temperature solution is carried out to liquid bin, mesh and micropore area, temperature concentration joint reconstruction algorithm is used to apply temperature change and local concentration effect to property parameter update, and through time series integral risk prediction algorithm, blockage risk is continuously accumulated and predicted;With micropore temperature difference index and local concentration factor as cross-step coupling variable, realize from temperature field to the whole-link moment-to-moment transfer of blockage risk, it is favorable to improve the continuity of particle size spectrum prediction and the stability of blockage risk window estimation under temperature change condition.

This invention belongs to the field of digital marketing technology. It discloses an intelligent analysis and optimization system based on short video platform traffic data, comprising: acquiring short video platform traffic trajectory data and interaction fingerprint features to construct a content performance profile library; classifying traffic flows and drawing a domain popularity star map; detecting algorithm pulses and user preference migration traces to form a fluctuation feature spectrum; interpreting historical traffic data, mapping seasonal traffic rhythm rules, and constructing a trend prediction model; applying it to target content to generate performance prediction results and transforming them into content frequency adjustment schemes; implementing real-time effect monitoring and establishing a feedback loop. This invention achieves intelligent analysis and automatic optimization of short video content delivery, accurately predicting content performance trends and dynamically adjusting delivery strategies, effectively improving platform traffic conversion efficiency.

This invention belongs to the field of robot localization and relates to an adaptive hierarchical robot localization method and apparatus for degraded scenarios. The method includes: real-time monitoring of the data quality acquired by heterogeneous sensors, mapping the states of the heterogeneous sensors to a unified quantization space, and outputting unique degradation level identifiers D1 to D4; for the D1 environment, uncertainty quantification is performed on feature matching, and the most contributing feature subset is selected; for the D2 environment, the degraded subspace is located through singular value decomposition; for the D3 environment, the expected error of the heterogeneous sensor engine is estimated through a performance prediction network, and continuous confidence weighting is used instead of hard switching; for the D4 environment, a cross-platform generalized pure inertial odometry is provided through a three-level architecture of pre-training-fine-tuning-online adaptation; and historical events are stored as empirical data. This achieves refined differentiation and directional processing of degraded scenarios and constructs an experience-driven closed-loop optimization mechanism.

This invention relates to the field of information security technology and proposes a hybrid encrypted data transmission and storage method and system adapted to the domestically developed IT environment. The method includes: training an adaptive neural network based on the hardware information of the domestically developed CPU to output a capability feature vector; using a cache prediction network based on the capability feature vector to predict the performance of national cryptographic algorithms and output a performance prediction matrix; using a deep reinforcement learning network based on the performance prediction matrix to detect resource conflicts between combinations of national cryptographic algorithms, filtering the combinations, and outputting the optimal combination of national cryptographic algorithms; encrypting the data to be transmitted and outputting encrypted transmission data; transmitting the encrypted transmission data to the target node, decrypting the encrypted transmission data, re-encrypting and storing it, and outputting securely stored data. This invention improves the security and adaptability of data transmission and storage in the domestically developed IT environment, and meets the security compliance requirements of the domestically developed IT environment through a complete transmission verification and storage re-encryption process.

This application provides a wafer performance prediction method, apparatus, electronic device, medium, and product, relating to the field of semiconductor technology. The method includes: acquiring initial sensor data corresponding to at least one target process step of the wafer to be processed; filling the initial sensor data with data to obtain target sensor data; the initial sensor data includes process parameters collected at different sampling time points; converting the target sensor data to obtain multiple target pixel images; the wafer to be processed corresponds to at least two target pixel images for each target process step; inputting the multiple target pixel images into a trained machine learning model to obtain a target prediction region; the target prediction region is a region composed of electrical parameters affecting wafer performance; and determining the performance of the wafer to be processed based on the target prediction region. The above process, by combining sensor data, image conversion, and machine learning technology, achieves effective prediction of wafer performance and improves the accuracy of prediction.

This invention discloses a machine learning-based reverse design method and system for hydrogel materials. The method includes: acquiring stress-strain curve data, material characteristics, and process parameters of hydrogel samples with different component ratios, and preprocessing them to construct a training dataset; establishing a performance prediction model based on the dataset regarding the relationship between hydrogel components and ductility, toughness, and maximum stress; introducing a target performance range to construct constraints, performing performance prediction and constraint screening on candidate component ratios; and comprehensively optimizing multiple performance indicators through joint error evaluation to obtain a hydrogel formulation scheme that meets the target performance range. This method combines an ensemble learning model with a range constraint optimization strategy to achieve reverse design with multi-performance synergistic optimization, improving material screening efficiency and design accuracy, reducing experimental trial-and-error costs, and is suitable for rapid formulation design and optimization of hydrogel materials and related biomedical materials.

A performance prediction method for an exercise product 18 comprises: a process for acquiring product identification information for identifying at least a category to which the exercise product 18, which is used by a user 10, belongs; a process for acquiring use information possibly affecting the performance of the exercise product 18 if the user 10 exercises while using the exercise product 18; a process for predicting a change in the performance of the exercise product 18 on the basis of the product identification information and the use information; a process for determining a notification to be provided to the user 10 on the basis of a prediction result; and a process for outputting the notification on the basis of the determination.

This invention provides a project design and optimization method, system, and storage medium. The method includes: acquiring initial project information; cleaning, normalizing, and performing correlation processing on the initial project information to obtain processed project information, wherein the processed project information includes processed historical project information and processed current project information; constructing a knowledge graph by processing the processed historical project information using a natural language processing algorithm and a fusion model; constructing a project performance prediction model using machine learning methods based on the processed historical project information and the knowledge graph; acquiring project requirements; determining constraints based on the project requirements; obtaining candidate projects based on the constraints, the project performance prediction model, and the processed current project information; and determining the final project based on the candidate projects. This application can improve design efficiency and reduce resource consumption.