30results about How to "Guaranteed prediction accuracy" patented technology

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.

The present application relates to a method for predicting fatigue crack propagation rate of a diffusion bonded dissimilar titanium alloy laminate structure, which is based on the mechanical properties of the constituent materials of the dissimilar titanium alloy laminate structure, i.e., the quasi-static tensile properties of the two titanium alloy plies. The method for predicting fatigue crack propagation rate of the diffusion bonded dissimilar titanium alloy laminate structure first establishes the relationship between the fatigue crack propagation rate of the laminate structure and the mechanical properties of the laminate structure, and then establishes the relationship between the mechanical properties of the laminate structure and the mechanical properties of the dissimilar titanium alloy single ply, thereby describing the relationship of the fatigue crack propagation rate of the laminate structure. Compared with the prior art, the method of the present application has the advantages of simple and convenient test, engineering precision, rapid and practical, etc., and effectively reduces the test cost and test time required for crack propagation test of the dissimilar titanium alloy laminate structure processed from different parameters.

This application provides a method, apparatus, electronic device, and storage medium for predicting battery heat generation power. It acquires multiple first average heat generation powers of the battery when charged and discharged at different rates at a target temperature, and multiple second average heat generation powers of the battery when charged and discharged at the target rate at different temperatures. The target temperature includes the middle temperature of the battery's operating temperature range, and the target rate includes the rate at the middle position of the battery's rate range and the rate at the beginning and / or end positions. The multiple first average heat generation powers and multiple second average heat generation powers are fitted to obtain a relationship between the heat generation power of the battery when charged and discharged at different rates at different temperatures. Based on the obtained heat generation power relationship, the heat generation power of the battery when charged and discharged at any rate at any temperature is determined. This application can save testing time and resources while ensuring prediction accuracy.

This invention discloses a data-driven method for monitoring marine phytoplankton abundance using underwater acoustic networks. The method includes: constructing and training a multi-level spatiotemporal feature ensemble model based on historical marine phytoplankton abundance and corresponding marine environmental data to determine the optimal spatial location and monitoring time window of the underwater acoustic network nodes; constructing a teacher model for channel prediction, including a multi-layer cascaded structure and residual modules, and compressing the teacher model into a student model using spatiotemporal knowledge distillation; training the student model to achieve real-time channel state prediction at the underwater acoustic network nodes; matching suitable underwater acoustic communication devices and seasonal optimal routing strategies to each underwater acoustic network node based on the optimal spatial location and real-time predicted channel state, and performing adaptive modulation and coding at the link level, while simultaneously monitoring phytoplankton abundance using the underwater acoustic network. This invention can improve data throughput and prediction accuracy under strict energy consumption constraints.

The application provides an aircraft wing skin corrosion depth prediction method based on a GAN and an improved LightGBM algorithm, which comprises the following steps: S1, data enhancement: four fully connected layers are used to extract feature information between columns; S2, an improved condition generator and discriminator are used, and parameters are updated; S3, the LightGBM algorithm is improved, and the aircraft wing skin corrosion depth is predicted. The aircraft wing skin corrosion depth prediction method using the generative adversarial network for data enhancement and the gradient regression model for sample regression can accurately predict small sample data. Meanwhile, the gradient regression model LightGBM algorithm is improved, which is more suitable for the prediction of the aircraft wing skin corrosion depth, and can greatly improve the prediction accuracy of the aircraft wing skin corrosion depth prediction.

This invention discloses a short-window gamma-gamma turbulence parameter prediction method for space-to-ground laser communication, belonging to the technical field of space-to-ground laser communication and atmospheric turbulence channel parameter prediction. This method addresses the problems of traditional methods, such as strong dependence on long observation windows, significant degradation in prediction accuracy under short-window scenarios, and insufficient robustness under low signal-to-noise ratio conditions. It establishes a short-window observation model for the space-to-ground optical link and a gamma-gamma channel statistical model, constructs time-dependent short-window training data, and designs a short-window gamma-gamma network. Temporal features are extracted through a convolutional backbone, and a scintillation exponential physical regularization auxiliary head is used to achieve joint parameter prediction under physical constraints, outputting predicted gamma-gamma distributed parameters. This method can achieve high accuracy and good stability in parameter prediction under short observation windows and low signal-to-noise ratio conditions, and can be used for turbulence channel state characterization at the space-to-ground laser communication receiver.

This application relates to the field of automotive technology, and more particularly to a method, apparatus, device, and storage medium for adjusting a model. The method includes: obtaining a first collision simulation result based on a whole-vehicle collision model of a vehicle; the first collision simulation result includes at least the impact force of a first barrier and a protective beam in the vertical direction and a first acceleration of the non-deformed position of the protective beam in the vertical direction; determining the equivalent mass that generates the impact force based on the impact force and the first acceleration; controlling a second barrier included in the simplified collision model to impact the protective beam included in the simplified collision model with the impact force based on a simplified collision model of the vehicle and the equivalent mass, thereby obtaining a second collision simulation result; determining a first distance based on the first collision simulation result; determining a second distance based on the second collision simulation result; and adjusting the simplified collision model based on the difference between the first distance and the second distance.

The application provides a floor internal stress prediction method and system, specifically, a floor image is acquired, finite element analysis is performed in combination with macroscopic load and boundary conditions to generate an initial stress field; visual key points in the image are extracted, key points in a high gradient area are selected as sparse key points according to a gradient of the initial stress field, and multi-modal feature representation is generated by fusing visual features and local stress features of the sparse key points; sparse key points are taken as nodes, a stress correlation graph with a weighted edge is constructed according to a spatial distance, the stress correlation graph and the multi-modal feature representation are input into a graph neural network, and stress values of each node are predicted; and a continuous internal stress distribution graph covering the entire floor is generated through a spatial interpolation algorithm.

The application discloses a dynamic human body falling detection method based on future human body time sequence posture prediction, and the method comprises the following steps: automatically calibrating the ground in a monitoring scene to obtain ground plane parameters; detecting, cropping and tracking the human body target in a video image to obtain a human body image sequence; performing single-person 3D posture estimation on the human body image sequence frame by frame to obtain a historical 3D skeleton sequence; constructing a skeleton motion flow feature according to the historical 3D skeleton sequence, and extracting a current human body state feature by using a time sequence feature coding model; predicting a future H-frame human body 3D skeleton sequence based on the current human body state feature; determining whether the human body has a risk of falling soon according to the included angle between the human body trunk vector in the future 3D skeleton and the ground plane normal vector, and combining the distance information of the skeleton key point to the ground plane; and outputting a warning signal when a preset condition is met, and the method is suitable for security monitoring, old-age care, public safety and the like.

This invention discloses a method and system for predicting lower limb joint angles, comprising the following steps: collecting first original joint angle time series from multiple healthy test subjects continuously performing various typical movements; collecting second original joint angle time series from multiple healthy test subjects performing any type of typical movement; standardizing the first original joint angle time series to obtain standard samples, wherein a portion of the standard samples constitutes the training set, and the remaining standard samples serve as the first samples; standardizing the second original joint angle time series to obtain second samples; using both the first and second samples as test samples, and all test samples constitute the test set. This invention can effectively improve the prediction accuracy and real-time computation efficiency of the prediction model, and enhances the cross-scene adaptability of the prediction model while ensuring prediction accuracy, facilitating practical use.

The invention discloses a water-rich rock mass fracture precursor identification method based on a lightweight neural network, and belongs to the technical field of deep rock mass engineering safety monitoring, and the method comprises the steps: collecting pulse waves and interference waves of deep rock mass fracture to form a data pair, building a waveform library based on the data pair, building a lightweight model for predicting rock fracture, and carrying out the prediction of rock fracture. The method comprises the following steps of: extracting and splicing data pairs to obtain a fused feature map, extracting local features, fusing to obtain a time sequence feature map, compressing by utilizing global pooling, converting into a nonlinear channel association vector, normalizing to obtain a weight vector, combining with the time sequence feature map, and compressing to obtain a channel statistical vector; and performing nonlinear feature transformation and numerical regularization by using a full connection layer, and obtaining ternary probability distribution through a Softmax function. According to the invention, key technical support is provided for real-time monitoring, and urgent demands of water-rich rock mass engineering on intelligent, real-time, high-reliability and safe monitoring are precisely met.

This invention discloses an adaptive distributed support vector regression method to combat Byzantine faults. First, the objective function of the SVR model is smoothed to construct an empirical risk minimization objective function. Then, with t=1, the master machine obtains initial parameter estimates on its local data. In the t-th iteration, a MOM-based aggregation method is used to obtain the MOM estimate, which is then used as the global gradient estimate. Subsequently, a robust global gradient is obtained based on the global gradient estimate. After obtaining the robust global gradient, the master machine updates the global parameters using gradient descent. Finally, it checks whether the convergence condition is met. If the convergence condition is not met, the above steps are repeated; otherwise, the globally optimal parameter estimate is obtained. By introducing a robust aggregation mechanism based on the MOM method, it can effectively suppress the influence of abnormal machines, significantly improving the stability and robustness of the system while ensuring prediction accuracy. It can be widely applied to various practical distributed systems.

The invention discloses a text prompt type heart function intelligent evaluation method based on a visual language large model. The method comprises the following steps that 1, an echocardiography mark data set is constructed; 2, dividing four chambers of the heart; step 3, hierarchical feature learning; 4, performing multi-scale feature fusion; and 5, predicting the cardiac function (ejection fraction). According to the method, the heart can be modeled into a multi-scale semantic system, and features from macroscopic chamber dynamics to microstructure motion are captured through a hierarchical feature learning mechanism. Deep integration of imaging data and clinical knowledge is realized by aligning hierarchical visual features with professional medical descriptions in a shared semantic space. The method has excellent performance in the aspect of ejection fraction prediction.

The application discloses a biological age evaluation method and system based on whole life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. First, the obtained original DNA methylation data is subjected to quality control and standardization processing; then, differential methylation sites significantly related to calendar age are screened as features; next, taking calendar age as a target variable, a regression model is constructed by adopting a LightGBM gradient boosting framework to obtain a methylation clock model; subsequently, the model is used to predict the DNA methylation age of an individual, and an epigenetic age acceleration value is calculated based on the deviation of the DNA methylation age from the calendar age; finally, the age acceleration value is subjected to correlation analysis with health or physiological indexes in different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The application covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

This invention discloses a video prediction model compression method and system based on optical flow-guided knowledge distillation, belonging to the field of computer vision technology. The method includes: generating optical flow distillation loss by constraining the optical flow prediction distribution of the student network to be consistent with that of the teacher network; generating channel alignment loss by aligning the channel attention distributions of the teacher and student networks using divergence; generating pixel-level reconstruction loss based on the difference in pixel values ​​between the generated image predicted by the student network and the real image; generating perceptual loss based on the difference in high-level semantic feature space between the generated image predicted by the student network and the real image; and obtaining the trained student network based on the optical flow distillation loss, channel alignment loss, pixel-level reconstruction loss, and perceptual loss. This invention can solve the performance degradation problem caused by neglecting spatiotemporal characteristics in existing compression methods for video prediction tasks.

The application discloses a production method and system of a lightly boron-doped substrate high-resistance wafer, and belongs to the technical field of production control. The method comprises the following steps: calculating a waste gas residual index based on ion concentration of waste gas; if abnormality occurs in a plurality of waste gas residual indexes in succession, updating model parameters of a layered state observer; otherwise, inputting online monitoring data into the layered state observer to obtain real-time distribution and uncertainty distribution of resistivity in a wafer growth process; setting an expected state evolution track, wherein the expected state evolution track comprises an ideal distribution matrix of resistivity at each preset time, calculating error distribution based on the real-time distribution and the ideal distribution matrix; obtaining an adjustment instruction based on fuzzy reasoning model processing of the error distribution and the uncertainty distribution, adjusting production process parameters based on the adjustment instruction; and generating an expected state evolution track of the next batch based on average quality error. The application can guarantee the stability of production quality.

This invention provides a reliability assessment method for the structural integrity of propellant grains in solid rocket motors. The method includes: constructing an initial training set through clustering and selective sampling; training a first kriging model; and iteratively updating the model to meet convergence conditions. Subsequently, a second kriging model is further trained based on the updated sample pool, which is ultimately used to calculate the failure probability of the propellant grain at its maximum strain. This invention achieves coordinated handling of stochastic and cognitive uncertainties in the propellant grain structural response by introducing a clustering screening and learning function-guided sample selection mechanism, as well as a two-stage progressive model update strategy. This method significantly reduces computational resource consumption while ensuring prediction accuracy, improving the efficiency and applicability of reliability assessment. It is particularly suitable for the integrity analysis of complex propellant grain structures with multi-source uncertainties, providing more accurate and efficient technical support for the reliability design and life assessment of solid rocket motors.

The application provides an organic crystal structure prediction method, device, equipment and storage medium, wherein the method comprises: generating at least one organic crystal structure according to the condition vector of a compound, determining the calculation index parameter of each organic crystal structure, and determining the prediction index parameter according to the molecular structure of the compound, and finally, obtaining the stable organic crystal structure and its ranking according to the calculation index parameter, the prediction index parameter, the index parameter difference threshold value of calculation and prediction, and each intermediate organic crystal structure. Through the generation model, potential organic crystal structure generation can be performed, more organic crystal structures can be explored, and the calculation complexity is greatly reduced. Through the discrimination model, the organic crystal structure screening can be performed, and the calculation amount can be effectively reduced. The application couples the generation model and the discrimination model for organic crystal structure prediction, significantly reduces the calculation complexity under the premise of ensuring the prediction accuracy, and has high practicability.

This application provides an optimization method, apparatus, and related products for accelerated aging testing of flash memory chips based on a lifetime prediction model, relating to the field of flash memory testing technology. The method involves collecting multi-dimensional test data related to flash memory lifetime; evaluating the contribution of each test feature in the multi-dimensional test data based on a flash memory lifetime prediction model to determine the degree of influence of each test feature on the preliminary lifetime prediction result, obtaining a contribution score for each test feature; determining core test features, secondary test features, and redundant test features based on the contribution scores of each test feature; optimizing the accelerated aging test scheme based on the core test features, secondary test features, and redundant test features to obtain an optimized test scheme; testing the flash memory chip under test based on the optimized test scheme to obtain a test dataset, and inputting the test dataset into the flash memory lifetime prediction model to obtain the final lifetime prediction result.

This invention discloses a spatiotemporal traffic flow prediction method based on dual-flow decoupling. The method first decomposes the temporal data of road network traffic flow into trend and periodic components using the exponential moving average method. Then, features are extracted and fused using a deep linear network and a local feature hybrid network with temporal block embedding to obtain the global temporal prediction component. After the traffic flow temporal data is encoded in the temporal domain by gated dilated convolution, multi-view spatial aggregation is performed using forward, backward, and normalized graph convolutions with adaptive adjacency matrices to obtain the local spatiotemporal prediction component. Finally, dynamic weights are generated by a gated network, and the two types of components are weighted and fused to obtain the final prediction result. This invention accurately separates long-term and short-term traffic flow features, adaptively balances global patterns and local details, improves the accuracy and robustness of long-term temporal prediction, and reduces computational complexity, making it suitable for intelligent traffic flow management scenarios.

This application provides a method and related apparatus for optimizing a rapid prediction model of the flow field response of a high-consistency pulper. The method includes: constructing a CFD flow field mechanism model that simulates the dynamic phase transition from Newtonian to non-Newtonian fluid based on cumulative strain using a user-defined function; constructing a response surface model and defining a parameter constraint space based on experimental design and CFD simulation of key parameters; constructing CFD data samples containing spatial features and time series; constructing a CNN-LSTM-Attention model and training it with the samples to obtain a rapid prediction model of the flow field response; and using a multi-objective whale optimization algorithm to perform parameter co-optimization on the rapid prediction model, outputting a Pareto optimal solution set as the optimal parameters of the model. This application integrates mechanism modeling, data-driven prediction, and intelligent optimization decision-making, which can achieve coordinated optimization of stirring time and energy consumption, effectively improve the simulation accuracy and optimization efficiency of the swelling process, and reduce equipment operating energy consumption and R&D trial and error costs.

The application discloses a service performance dynamic prediction method and system and an intelligent energy consumption device, and relates to the technical field of civil engineering structure monitoring. The method comprises the following steps: acquiring multi-source data of a stress key structure; the multi-source data comprises data collected by an intelligent energy consumption device, and the intelligent energy consumption device comprises a spring of a mechanical metamaterial; inputting the multi-source data into a pre-constructed service performance prediction model to predict a current structure state of the stress key structure; wherein the service performance prediction model is an interpretable machine learning algorithm fused with physical constraints, and the physical constraints are related to the dissipated energy, stored energy and load-displacement relationship in the elastic recovery stage of the spring; and evaluating the residual bearing capacity of the stress key structure according to the predicted current structure state. The service performance prediction model fused with the physical constraints and the machine learning algorithm can realize real-time safety evaluation and early warning of the service process of the civil engineering structure.

This invention provides a method and system for predicting the cobalt extraction rate in waste lithium battery solutions, belonging to the field of process parameter prediction in the recycling of waste lithium batteries. The method includes: constructing and preprocessing a cobalt extraction rate dataset including raw material composition, extractant composition, and extraction reaction condition parameters; training and comparing the performance of various tree models under default hyperparameter conditions to select a target prediction model; constructing a comprehensive evaluation loss function that simultaneously reflects prediction accuracy and overfitting; optimizing the model's hyperparameters within a preset hyperparameter range using the comprehensive evaluation loss function as the optimization objective; and performing a generalization test on the optimized model using unknown leachate parameters to output the cobalt extraction rate prediction result. This invention improves the stability and applicability of the prediction model under unknown process conditions by introducing a comprehensive evaluation mechanism.

This application discloses a training method, usage method, apparatus, device, and medium for an image classification model, belonging to the field of artificial intelligence. The image classification model includes a feature extraction network and a multiple instance learning model. The method includes: acquiring a sample image set, wherein each sample image in the sample image set includes at least two instances; training the feature extraction network using the sample images in the sample image set through self-supervised learning based on contrastive learning, obtaining a trained feature extraction network; and training the multiple instance learning model using the sample images in the sample image set through multiple instance learning based on a mutual attention mechanism, obtaining a trained multiple instance learning model. The above scheme can reduce the computational complexity of the image classification model. The embodiments of this application can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.

The application discloses a bridge structure deformation real-time prediction method based on finite element analysis, which comprises the following steps: a coupling model containing a bridge structure and a rock-soil finite element model is established; a proxy model of a graph neural network is introduced to reproduce the mechanical response of the rock-soil model; in the real-time prediction stage, an asynchronous time step framework is adopted, the bridge structure model is iterated at a small step, and the proxy model provides interface reaction force at a large step, so that the calculation efficiency is improved; by receiving the actual monitoring deformation data of the bridge, the key parameters of the rock-soil finite element model are reversely calculated and updated based on the Bayesian inference theory, the initial parameter deviation and the model time-varying effect are eliminated, the retraining of the proxy model is triggered after the parameter updating, and the prediction model is ensured to be long-term synchronized with the real state of the bridge; the application solves the problem that the traditional finite element analysis is difficult to consider real-time performance and long-term accuracy, and provides reliable prediction for the whole life cycle health monitoring and prospective maintenance of the bridge structure.

The application discloses a kind of cross-modal time series prediction methods, systems, medium and equipment based on asymmetric fusion, comprising the following steps: S1, obtains input data;S2, respectively extracts time series modal features and text modal features from historical time series data, historical text prompts and future text prompts by single-modal feature extraction module;S3, the historical text modal feature is fused and handled in the time series modal feature space by asymmetric fusion module, and cross-modal consistent feature and modal unique feature are calculated;S4, the historical text modal feature and the future text modal feature are fused by history-future text fusion module, and the fusion text feature containing prediction period priori knowledge is obtained;S5, the fusion text feature is decoded, and the future time series prediction result of prediction target time period is output.The present application provides more sufficient priori support for prediction period.