137 results about "Age prediction" patented technology

Provided is a fuel cell aging prediction method, a system thereof and a medium. The method includes the following steps: acquiring operation parameters and calculation parameters of a fuel cell corresponding to each historical moment in a historical time period, where the calculation parameters include a stack voltage, active area and a hydrogen permeation current; carrying out calculation based on the calculation parameters corresponding to the historical moment to obtain a mixed aging index corresponding to the historical moment; taking the operation parameters and the mixed aging index corresponding to each historical moment in the historical time period as inputs, and predicting the mixed aging index corresponding to the prediction moment by a mixed aging index prediction model.

The invention provides an equipment operation state prediction method and system based on digital twinning. The method comprises the following steps: firstly, constructing a multi-physics field digital twinning model comprising an equipment independent model and a coupling relation between equipment; a target stress event that causes damage to the device is identified by processing multi-modal sensor data in real time. Once identified, simulation deduction is performed in the twin model, the simulation simulates the propagation, transformation and accumulation process of the stress in the device network based on the dynamically adjusted stress conduction weight, and calculates the accumulated stress of each associated device. And finally, converting the accumulated stress into an equivalent aging increment of each device in combination with a preset damage model, updating a health state baseline of each device, and generating a prediction report of a future associated aging risk. According to the method, the conduction and accumulation effects of the stress in the equipment network are simulated, so that the spanning from monomer monitoring to system-level associated aging prediction is realized, and the accuracy of equipment state prediction is greatly improved.

The invention discloses a biomarker combination for predicting calendar age, a model and application of the biomarker combination. 57 kinds of DNA methylated CpG sites screened from many elderly subjects are creatively introduced, through multiple detection and large-scale parallel DNA sequencing, operation is easy, the result is highly repeatable, and the age prediction model constructed based on the method is good in stability and accuracy and high in sensitivity and has good application prospects. Calendar age can be predicted from trace blood samples associated with forensic cases.

The invention relates to the field of marine engine performance monitoring and optimization, and discloses a marine engine adaptive aging prediction method and a fault diagnosis model.The prediction method comprises the following steps that a digital twin model of a marine engine is constructed, and an engine reference health state is generated; acquiring operation parameters of the marine engine in real time, and inputting the operation parameters into the digital twin model to calculate a reference performance index; identifying the degradation offset of the actuating mechanism by comparing the output of the digital twin model with the operation data of the entity engine; constructing a multi-objective optimization problem based on performance indexes of a digital twin model, and generating a Pareto optimal solution set by using an NSGA-III algorithm; and an optimal solution under a specific working condition is selected through a fuzzy comprehensive evaluation method, and degradation state evaluation and optimal control strategy generation are realized. Compared with the prior art, the method can accurately predict the degradation state of the marine engine, reduces the maintenance cost, improves the operation efficiency and reliability, and meets the dynamic optimization requirements of complex working conditions.

The invention discloses a human body life health management method and system based on AI twinborn digital humans, and the method comprises the steps: collecting human body life health data in multiple dimensions, comprehensively and deeply collecting human body health information, breaking through the limitation of single traditional health management data, and providing a rich data basis for precise health analysis; through combination of an artificial intelligence model and a medical professional field knowledge base, deep mining and intelligent analysis can be performed on complex health data, multi-system / organ physiological age prediction, early risk prediction of diseases, accurate diagnosis and personalized health guidance are realized, and scientificity and effectiveness of health management are improved; based on the AI twinborn digital human generated by the user in a personalized manner, health management service is provided for the user in a visual and intimate manner, the sense of participation and acceptability of the user are enhanced, and the requirements of the user for personalized health service are met, so that the method has the advantages of high intelligent degree and good interaction effect.

The invention relates to a P91 pipeline aging prediction method, and belongs to the field of P91 pipeline aging prediction. The method comprises the following steps: acquiring a micro-magnetic signal (such as Barkhausen noise) of a P91 pipeline through a micro-magnetic detector, and extracting characteristic parameters related to an aging state; and inputting the parameters into a pre-trained aging grade prediction model, and directly outputting the aging grade (1-5 grade) of the pipeline. The prediction model is constructed based on a machine learning algorithm (such as ReliefF feature selection and a BP neural network), and training data comes from an accelerated aging sample which is simulated through specific heat treatment and has a clear microstructure (represented by EBSD) and macromechanical property correlation. According to the method, rapid, lossless and quantitative prediction of the aging state of the P91 pipeline is realized, and a reliable basis is provided for safety assessment and residual life prediction of the in-service pipeline.

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

The invention discloses an SBS (styrene butadiene styrene) modified asphalt aging performance evaluation method, a prediction model and application, relates to a construction method of an SBS modified asphalt aging performance evaluation and prediction model based on multi-scale aging evaluation indexes, and aims to solve the problems that aging evaluation depends on a single-scale index and the prediction precision is insufficient in the prior art. The model is verified by accelerated aging data in a laboratory, the thermo-oxidative aging error is less than or equal to 9.4%, and the ultraviolet aging error is less than or equal to 7.5%. Engineering application: providing road maintenance period suggestions (for example, when the aging time is predicted to be 5 years, detection is recommended once every two years); triggering a material replacement threshold value when the number of honeycomb structures drops to 60% of the initial value; the performance recovery is realized by compounding the new asphalt and the regenerant (the mixing amount is 10-15%). Through multi-scale index collaborative analysis and data modeling, the aging prediction precision is significantly improved, and a scientific basis is provided for asphalt material life evaluation and road maintenance decision.

The invention discloses an anti-aging prediction method and system for an automobile paint surface protective film based on microelement analysis, and relates to the technical field of anti-aging evaluation of bonding materials, and the method comprises the steps: based on the automobile paint surface protective film, arranging a microcell spectrum probe array, and setting a multi-spectrum cooperative detection coordinate system; constructing a dynamic material degradation characteristic spectrum; meanwhile, a dynamic environment simulation cabin is configured, and an accelerated aging data set is obtained; and simulating a quantitative relation between a micro-element migration track and a mechanical property degradation rule, and carrying out cross-scale reverse prediction on the residual service life of the automobile paint surface protection film. According to the method, the technical problems that in the prior art, the micro element valence state distribution of the automobile paint surface protection film is difficult to accurately analyze, the residual service life prediction lacks a cross-scale analysis means, and the aging prediction accuracy is insufficient are solved, the micro element valence state distribution analysis of the automobile paint surface protection film and the cross-scale reverse prediction of the residual service life are realized, and the aging prediction accuracy is improved. And the aging prediction accuracy of the protective film is improved.

The invention relates to the technical field of precision timing instrument testing, in particular to an atomic clock aging prediction method based on feature fusion and quantum constraint learning. According to the atomic clock aging prediction method based on multi-modal feature fusion and quantum constraint learning provided by the invention, a multi-modal tensor fusion means is adopted, and a time domain microwave probe signal, a space domain atomic cloud density distribution signal and a frequency domain lambda stripe signal are collected at the same time for feature extraction, so that the completeness of the features is improved; during nonlinear modeling, a Bloch equation constraint is adopted to ensure that a network model accords with a quantum physics rule. According to the method, the atomic clock aging feature expression effect can be improved through an artificial intelligence means, the defect of overfitting caused by purely depending on training data is effectively avoided, the atomic clock aging prediction model better conforms to the physical law, the atomic clock aging prediction precision is improved, and the prediction model complexity is optimized.

The invention relates to the technical field of cable joint aging prediction, and particularly provides a power distribution network cable joint aging prediction method and system, and the method comprises the steps: obtaining the multi-mode operation data, joint type information and operation condition information of a to-be-predicted cable joint; performing aging feature extraction on operation data of different modes in the multi-mode operation data based on a preset aging feature extraction rule to obtain joint aging features corresponding to the different modes, and then performing feature fusion on all the joint aging features to obtain fused aging features; determining target parameters of an aging degree prediction algorithm according to the joint type information and the operation condition information; performing aging degree prediction according to the fused aging features by using an aging degree prediction algorithm to obtain an aging prediction result; according to the method, the aging degree prediction algorithm can have dynamic adaptability to real-time operation conditions and joint types.

Provided herein is a method of using digital twins for medical diagnosis, prediction, and / or prognosis of a disease or condition, such as diagnosis, prediction, and / or prognosis of one or more cancers, organ-specific age prediction, predicting of female aging, predicting hospital-acquired infection, predicting dialysis time series, predicting diabetes and complications, predicting pregnancy and child outcome, predicting heart failure, predicting myopia, predicting vision in anti-VEGF treatment, or any combination thereof. A system and non-transitory computer-readable storage medium configured for performing the method is also disclosed.

The invention discloses a new energy equipment part aging prediction method and system based on digital twinning and multi-source data fusion, and relates to the technical field of digital twinning and intelligent prediction of new energy equipment, and the method comprises the steps: building a digital twinning model, and carrying out the working condition label labeling of multi-source monitoring data according to the current operation working condition of the equipment; extracting working condition aging characteristic parameters, calculating change gradients and working condition switching stress factors, and storing the change gradients and the working condition switching stress factors into a working condition associated aging characteristic library; inputting the working condition aging characteristic parameters and the working condition switching stress factors into a cross-working condition aging accumulation model, and outputting an aging accumulation index; and taking the aging accumulation index as a simulation initial state, simulating an attenuation trajectory of a component performance parameter under a preset future working condition sequence, when the performance parameter reaches a preset failure threshold value for the first time, extracting a running time length, and taking a difference value between the running time length and a current actual running time length as a component aging prediction remaining life. According to the invention, prospective residual life prediction based on the current aging state is realized.

The invention provides a memristor memory chip aging prediction method and a computing device based on artificial intelligence, and belongs to the technical field of integrated circuits, the method comprises the steps of obtaining multi-dimensional device operation data by monitoring a device erasing process when a memristor memory chip executes a reasoning task in a working process, and performing aging prediction on the memristor memory chip; and constructing a data set containing the aging characteristics of the device, and predicting and classifying the aging state of the device based on an artificial intelligence algorithm. The computing device comprises a plurality of memristor storage / storage macros and a global aging prediction control / processing module, the memristor storage / storage macros can be configured as reasoning computing units for aging prediction, and real-time aging state prediction is carried out on other memristor storage / storage macros for processing actual edge AI tasks. And real-time aging state prediction can be carried out by the global aging prediction control / processing module. Through data-driven aging prediction and a systematic load balancing strategy, the overall service life and reliability of the memristor storage system can be effectively improved.

The invention provides a multi-modal data fusion teenager age prediction method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-predicted oral cavity panorama and gender data, and carrying out the preprocessing; predicting the age of the oral panoramic film and the gender data by using a multi-modal fusion age prediction model; wherein the multi-modal fusion age prediction model adopts a pre-trained improved neural network, a first convolutional layer of the improved neural network adopts a single channel to input an oral cavity panorama to extract image features, and the encoded gender data and the image features are fused through a full connection layer. According to the invention, the age of the teenagers can be accurately and quickly predicted.

The present application relates to a model for predicting the age of a rat, which considers data of immune cells, or data of immune cells, and data of cytokine and / or chemokine levels to predict the age of a rat. The model of the present application significantly improves the accuracy and reliability of age prediction, provides strong support for personalized health management, and helps to promote the development of precision medicine, and ultimately aims to achieve the goal of extending healthy life.

This application belongs to the field of virtual art restoration technology, specifically providing a deep learning-based virtual art restoration system. The system primarily analyzes historical images and environmental data of the artifact and restoration materials to train an aging prediction model and simulate long-term appearance changes. By quantitatively comparing the differences and stability of their aging trajectories, it calculates the long-term compatibility score for each restoration material and automatically selects the optimal material to generate the final virtual restoration image. This application effectively solves the problem in existing technologies where it is difficult to predict significant visual differences that may appear between restoration materials and the artifact itself after long-term natural aging, ultimately leading to insufficient durability of the restoration results. It achieves scientific prediction and optimized selection of the long-term visual compatibility of restoration schemes, significantly improving the reliability and stability of restoration results and reducing the risk of restoration failure due to material aging incompatibility.

The invention discloses a multi-parameter MRI brain age prediction method and device based on uncertainty perception, equipment and a medium, and the method comprises the steps: determining a first feature sequence of an sMRI image, a second feature sequence of a DTI image, and a third feature sequence of an fMRI image through a feature extraction module; and determining a GM sampling vector, a DTI sampling vector and an fMRI sampling vector based on the first feature sequence, the second feature sequence and the third feature sequence by using a probability distribution encoder, and performing brain age prediction by using the GM sampling vector, the DTI sampling vector and the fMRI sampling vector. According to the method and the device, the fusion feature vector determined by fusing the sMRI, DTI and fMRI information is utilized to perform brain age prediction, so that multi-dimensional information of the brain structure and function can be more comprehensively acquired, and the accuracy of brain age prediction is improved. Meanwhile, a probability distribution encoder is introduced in the process of fusing the multi-parameter MRI images, multi-parameter MRI feature mapping is converted into Gaussian distribution through the probability distribution encoder to model feature uncertainty, richer potential information in the multi-parameter MRI images is captured, and the accuracy of brain age prediction is further improved.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The application discloses a kind of based on DNA methylation's construction method of biological age prediction model, the application is downloaded from GEO data website, derived from Chinese population, contains calendar age data, whole blood sample 450k methylation chip data, by the method of elastic network combined bootstrap, 31 methylation sites of modeling candidate are screened, multiple linear regression, support vector machine, random forest and gradient boosting regression tree are used to carry out preliminary construction and evaluation of model, then further filter methylation sites using full subset regression, obtain methylation age prediction model based on 18 methylation sites.Finally, using any provincial team natural population data, methylation age prediction model is optimized, and finally the biological age prediction model based on 18 methylation sites is obtained.The model is suitable for Chinese population, the number of methylation sites contained is less, is not affected by blood cell components, and the prediction accuracy is good.

The invention provides a marker for evaluating biological age, a probe composition and application thereof. The marker is an SCGN gene and / or a TRIM59 gene. According to the present invention, with the application of the marker, the methylation state of the genes can be sensitively and specifically detected, and the marker is used for constructing the age prediction model based on DNA methylation so as to detect the biological age.

The invention discloses a brain age prediction method based on spatial heterogeneity and time continuity modeling. The method comprises the following steps: 1, acquiring brain magnetic resonance imaging data and a real age label thereof; 2, the brain magnetic resonance imaging data passes through a brain age prediction backbone network to obtain whole brain feature representation and an age prediction result of a brain region unit; 3, inputting the whole-brain feature representation, the age prediction result and the real age tag into a comparative learning module of regional perception to obtain comparative learning loss; 4, training the brain age prediction backbone network according to the contrast learning loss, and 5, initializing the brain age prediction backbone network into a teacher model and a student model in a model reasoning stage, and obtaining more accurate brain age through the processing of the teacher model and the student model. According to the method, spatial heterogeneity and time continuity in the brain aging process can be modeled, and the accuracy of brain age prediction is improved.

The embodiment of the invention provides a physiological age prediction method and device, equipment and a storage medium, relates to the technical field of computers, can realize accurate prediction of physiological age by integrating external display images and internal medical examination data of human beings, and realizes efficient prediction calculation without contacting a detected person. The method provided by the embodiment of the invention comprises the following steps: acquiring face image data of a detected person, examination data of at least one human body physiological index and a real age; detecting a wrinkle area from the face image data, and determining an appearance aging factor of the detected person according to the detected wrinkle area; determining a body aging factor of the detected person according to the examination data; and obtaining a physiological age prediction result of the detected person according to the appearance aging factor, the body aging factor and the real age.

The invention relates to a face aging prediction method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a face image of a target user, actual life information of the target user and hypothetical life information; performing aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user based on actual conditions; and performing aging prediction on the target user according to the face image and the hypothetical life information to obtain an aging prediction result of the target user based on a hypothetical condition.

The application relates to the technical field of precision timing instrument testing, in particular to an atomic clock aging prediction method based on feature fusion and quantum constraint learning. The atomic clock aging prediction method based on multi-modal feature fusion and quantum constraint learning provided by the application adopts a multi-modal tensor fusion means, simultaneously collects time domain microwave probe signals, space domain atomic cloud density distribution and frequency domain Ramsey fringe signals for feature extraction, and improves the completeness of the features; when a nonlinear model is established, Bloch equation constraint is adopted to ensure that the network model conforms to quantum physical laws. The method can improve the atomic clock aging feature expression effect through an artificial intelligence means, effectively avoids the overfitting defects caused by simply relying on training data, makes the atomic clock aging prediction model more conform to physical laws, and improves the atomic clock aging prediction precision and optimizes the prediction model complexity.

The invention discloses a PID effect accelerated aging prediction method for a component, and aims to solve the problems of individuation and mechanism uncertainty in component aging prediction. The method comprises the following steps: firstly, establishing a component gene vector containing multi-source heterogeneous attributes for each component so as to instantiate a digital twin; secondly, constructing a shared causal guided generative model taking a physical causal map as a structural constraint as a prediction core; in dynamic operation, the system performs probabilistic path prediction on a single twinborn body, and dynamically calibrates the state of the twinborn body by using new measured data through a data assimilation technology. Furthermore, the system inversely optimizes the physical causal atlas by using a structure learning algorithm by aggregating evolution data of the whole twinborn cluster, so as to realize iterative upgrade of the prediction core. According to the method, individual self-adaptive calibration and swarm intelligent evolution are combined, so that the system has the capability of discovering and perfecting physical laws from data, and the accuracy and robustness of long-term prediction are remarkably improved.

The invention provides a female age prediction system and method fusing facial images and biochemical indexes, and belongs to the field of artificial intelligence and health management, and the system comprises a data collection and preprocessing module, a dual-channel feature extraction module, a multi-modal feature topology fusion module, an age prediction and health evaluation module, and a personalized health intervention module. According to the method, a biochemical feature topological space and a facial feature topological space are innovatively constructed, feature fusion is realized through a cross-modal attention interaction network and a topological consistency keeping mechanism, the system predicts the biological age and generates a three-dimensional health risk score based on the fused features, and dynamic calibration is carried out in combination with female physiological cycle data, so that the health risk of the female is evaluated. According to the method, the technical contradictions that the traditional detection method is high in cost due to high precision and obvious in error due to low cost are solved, and accurate and reliable female physiological age evaluation is provided.

The invention discloses a plateau population brain aging comprehensive index determination method and device, a medium and a computer program product, which adopt a 3D-CNN architecture to process nerve image data, improve brain age prediction precision, apply a longitudinal comparative analysis method, realize individualized brain aging trajectory tracking, generate a saliency map, and improve brain age prediction accuracy. And visual abnormal brain region positioning information is provided. And a multi-modal weighted fusion algorithm is adopted, so that data isomerism challenges are overcome, and information complementation and result verification are realized. Brain oxygen saturation parameters are incorporated into a brain aging evaluation system, and the key problem that hypoxia affects brain aging in a plateau environment is solved. A plateau population brain aging norm is established, the problem that plain standards are not applicable is solved, and a plateau brain aging specific mode is disclosed. The integrated evaluation process simplifies the operation steps, reduces the professional threshold, improves the prevention and treatment effect through personalized intervention suggestions, and is beneficial to delaying the brain aging process.

The application discloses a method for predicting biological age based on enteroscopy pictures, and belongs to the field of digestive endoscopy image analysis. The method first screens enteroscopy pictures without abnormalities, and completes quality control and edge cutting; then constructs an image package with multiple pictures in a single report, extracts features and weighted aggregation by using a multi-instance learning model based on attention; and converts age prediction into a classification task and introduces a Gaussian soft label to realize high-precision biological age prediction. The application can mine aging-related features from enteroscopy images without obvious abnormalities, does not need additional equipment and invasive operation, has strong generalization, and can be used for risk stratification of enteroscopy-negative people and precise prevention and treatment of colorectal cancer.

The application discloses a planar transformer aging prediction parameter correlation analysis method, system and device, and relates to the technical field of planar transformers. The application receives change data of to-be-analyzed parameters of a planar transformer at different time points; takes data recorded at zero time as a standard value to normalize the rest of the data; processes the normalized data through curve fitting to take parameter data with a slope closest to 1 as a reference sequence; uses a grey correlation degree analysis method to calculate correlation coefficients of the rest of the parameters and the selected reference sequence, and normalizes the correlation coefficients. The application uses the grey correlation degree analysis method to comprehensively apply multiple parameters of the planar transformer, provides a theoretical basis for weight distribution when the influence of the multiple parameters on the prediction process is comprehensively considered, and finally constructs a planar transformer life prediction model according to a mathematical statistics method, thereby improving the accuracy and reliability of planar transformer aging prediction.