175 results about "Age prediction" patented technology

The invention discloses a submarine cable insulation multi-field aging prediction method and system, and relates to the technical field of material science and engineering. An existing model has the problems that parameter interaction effect analysis is insufficient, feature weight distribution is not scientific, a data standardization method is insufficient, and the capacity of capturing dynamic response and nonlinear aging behaviors is weak. The method comprises the steps of multi-physics field accelerated aging experiment design and data acquisition, key characteristic parameter screening through grey correlation analysis, support vector machine modeling and optimization, dynamic aging behavior evaluation and service life prediction and feedback adjustment. According to the technical scheme, aging behaviors are comprehensively captured through a multi-physics field cooperation experiment, key parameters are scientifically screened in combination with grey correlation analysis, a nonlinear relation is optimized through support vector machine modeling, long-term prediction precision is improved through dynamic feedback adjustment, and reliability is enhanced through quantitative life evaluation; the aging characteristics of the submarine cable insulating material in multiple physical fields are comprehensively and accurately reflected, and a scientific basis is provided for multi-dimensional evaluation of the aging performance of a high polymer material.

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 provides a loss prediction method and system for a light building material. Wherein the long-term change trend of the environment where the light building material is located is analyzed and processed, and an analysis result is obtained; according to the analysis result, key environmental factors having significant influence on aging of the light building material are identified, and the potential loss risk level of the building material is obtained; based on the potential loss risk level, mining historical data laws extracted from maintenance records, user feedback and a historical case library by using a time sequence analysis technology, and generating a performance degradation estimation curve; and making a maintenance plan and a replacement time table of the light building material by comprehensively considering functional requirements, economic cost and safety standards of the building according to the performance degradation estimation curve. According to the technical scheme provided by the invention, the aging prediction precision and the maintenance efficiency of the light building material are remarkably improved.

The invention discloses a figure scene generation system based on AI interaction. The figure scene generation system comprises a user interaction design module, a figure image shooting module, a figure image analysis and processing module, a figure image processing module, an image integration and correction module, an image generation module and a management database. The figure image shooting module is used for acquiring a user image and a microstructure of a skin surface by using a high-definition camera, the microstructure comprises a skin glossiness reference value, a wrinkle level reference value, pore characteristics and pigmentation, and the user image contains a hair coverage rate; according to the method, an algorithm is used for calculating pore features, color conversion is carried out on a microstructure to obtain pigmentation, a user age evaluation correlation coefficient is obtained through calculation of skin glossiness, a wrinkle level, the pore features, the pigmentation and a hair coverage rate, and a more accurate age evaluation result is provided by improving an age prediction method. The features of skin texture, pigmentation and the like can be quantified more accurately, so that the accuracy of age assessment is improved.

The invention discloses a method and device for generating an aging failure prediction model of a component and electronic equipment, and relates to the technical field of electronic equipment. Multiple groups of state data of the component under multiple tests are adopted when the aging failure prediction model is generated; therefore, the aging failure prediction model generated by the invention can predict the residual life of the component in different working environments, and the limitation that the aging failure prediction model in the related technology can only realize aging prediction of the component in a single working environment is improved; and the accuracy of predicting the residual life of the component by the aging failure prediction model is improved.

The invention discloses a cardiovascular system physiological age prediction method based on machine learning, and the method comprises the steps: specifically collecting cardiovascular health data, carrying out the modeling of a computer model for male and female data according to the cardiovascular health data, building a computer model of a male and female cardiovascular system, and carrying out the prediction of the physiological age of the cardiovascular system. The method is used for evaluating physiological ages of cardiovascular systems of individuals with different genders. According to the cardiovascular system physiological age prediction method based on machine learning, core data is modeled only by using blood pressure and heart rate indexes related to the cardiovascular system, and the physiological age model of the cardiovascular system is constructed, so that the cardiovascular physiological age can be evaluated in a targeted manner; moreover, according to the cardiovascular system physiological age prediction method based on machine learning, modeling is carried out for men and women based on the data difference of the genders of the men and the women, so that the prediction result is more accurate.

The invention provides a battery aging prediction method and system based on data analysis, and relates to the field of battery aging prediction, and the method comprises the steps: carrying out the clustering of use features, obtaining a multivariate use feature ratio, and constructing a battery aging predictor based on the multivariate use feature ratio; performing extreme driving compensation analysis according to the real-time driving data, and outputting a driving compensation coefficient; inputting the battery monitoring data, the real-time driving data and the environment data into a battery aging predictor, and outputting a predicted battery capacity attenuation proportion; and correcting the predicted battery capacity attenuation proportion according to the driving compensation coefficient to obtain a corrected battery capacity attenuation proportion as a prediction result. The method and the device aim at solving the technical problem that a prediction result is not accurate enough due to the fact that a traditional battery aging prediction model is usually trained based on data under a conventional driving condition and extreme driving behaviors possibly occurring in actual driving are ignored, and the accuracy and the reliability of battery aging prediction under the extreme driving condition can be effectively improved.

The invention provides a voice recognition method, device and equipment and a computer readable storage medium. The method comprises the following steps: acquiring to-be-recognized voice, a trained voiceprint recognition model and a trained age prediction model; performing feature extraction on the to-be-recognized voice to obtain to-be-recognized voice features; performing prediction processing on the to-be-recognized voice features by using the trained voiceprint recognition model to obtain a first voiceprint vector corresponding to the to-be-recognized voice; performing prediction processing on the first voiceprint vector and the to-be-recognized voice feature by using a trained age prediction model to obtain an initial prediction result; and determining a target prediction result of the to-be-recognized voice based on the initial prediction result, and outputting the target prediction result. The voice recognition method and device can be applied to an anti-addiction system, and the accuracy of voice recognition can be improved through the voice recognition method and device.

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 the technical field of fuel cells, and discloses a fuel cell aging prediction result uncertainty quantification and decomposition method, which comprises the following steps: acquiring and preprocessing voltage data; modeling and training of a B-MIXGU aging prediction model are carried out; aging prediction and result analysis; and uncertainty quantization and decomposition: quantizing the total uncertainty of the aging prediction result by using the variance of interval estimation, decomposing the total uncertainty into accidental uncertainty and cognitive uncertainty, and exploring the source of the uncertainty. According to the uncertainty quantification and decomposition method for the aging prediction result of the fuel cell, the predicted total uncertainty is decomposed into cognitive uncertainty and accidental uncertainty, and credibility evaluation of the aging prediction result is realized according to the uncertainty decomposition result.

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 relates to a battery aging prediction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring sample aging data of a sample battery; performing data enhancement processing on the sample aging data to obtain the sample aging data after data enhancement; extracting features in the sample aging data after data enhancement; inputting the features into a to-be-trained model, comparing predicted aging data output by the to-be-trained model with the sample aging data after data enhancement, and performing training according to a comparison result to obtain a trained battery aging prediction model; obtaining aging data of the to-be-tested battery; extracting features in the aging data of the to-be-tested battery; and inputting the characteristics into a battery aging prediction model to obtain a future change trend of the aging data of the to-be-detected battery. By adopting the method, the cost of the battery aging prediction method can be reduced, and the effects of low energy consumption, short training time and acceptable prediction precision are achieved.

The present application discloses a method, device, medium and product for determining a comprehensive brain aging index, relating to the field of image data processing. The method includes: preprocessing images such as T1 and TOF-MRA to obtain brain tissue segmentation images; post-processing TOF-MRA magnetic resonance images to extract cerebrovascular imaging features; using a method combining modules such as asymmetric convolution to perform convolution feature extraction on T1 and T2-FLAIR images, and compressing the features to generate a convolution feature group; after connecting demographic indicators, cerebrovascular imaging features and the convolution feature group, using a biological age prediction model to obtain a brain aging age prediction result, and combining with the actual age to obtain a comprehensive brain aging index. The present application can identify changes in large intracranial blood vessels, and can also reflect the state of earlier microvascular aging, improve the sensitivity to microvascular aging, solve the limitations such as low efficiency and poor consistency of manual feature extraction, and achieve comprehensive quantitative evaluation of brain aging.

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.

The invention relates to the technical field of judicial expertise, in particular to a segmented blood DNA methylation age prediction method and system based on an SVR model, and the method comprises the steps: collecting a DNA sample of a to-be-evaluated object; measuring the methylation level of a predetermined CpG site on the DNA sample to obtain a methylation level parameter; and predicting the age of the to-be-evaluated object according to the methylation level parameter. In order to solve the problem that the prediction error of a prediction method in the prior art is obviously increased along with the increase of age groups, in the scheme, firstly, the age interval of a to-be-evaluated object is predicted, then a prediction model on the corresponding interval is called in each age interval, and effective point locations screened out in the regression analysis process are combined, so that the prediction accuracy of the to-be-evaluated object is improved. The age of the to-be-evaluated object is accurately predicted, and the influence of the age interval on the age prediction error is improved.

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.