Chrome-molybdenum-vanadium steel creep life prediction method based on data driving

Abstract

The invention discloses a chromium-molybdenum-vanadium steel creep life prediction method based on data driving, which comprises the following steps: S1, data acquisition: converting the acquired data into a structured digital format, and manually marking the data; s2, performing sample generation by adopting a hyperbolic space embedding-based generative adversarial network algorithm, and further expanding the data; s3, adopting a neural network algorithm based on dynamic group evolution optimization as a feature extraction model, and performing feature extraction model training on the expanded data; s4, adopting a self-encoding neural network based on feature refinement as a feature dimension reduction model, and performing feature dimension reduction model training on the data after feature extraction; s5, adopting a fractional order neural network based on quantum state transfer as a classifier model, and performing classifier model training on the data after dimension reduction; and S6, processing and predicting a new sample by using the completely trained model. According to the method, the prediction capability of the creep life of the material is improved.

Description

Technical Field

[0001] The invention relates to a data-driven method for predicting the creep life of chromium-molybdenum-vanadium steel. Background Art

[0002] Chromium-molybdenum-vanadium steel is widely used in the manufacture of equipment under high temperature and high pressure environments, such as power plant boilers and petrochemical equipment, due to its excellent high temperature performance and creep resistance. However, as the equipment operates under high temperature stress conditions for a long time, the material will creep, resulting in performance degradation or even failure. Therefore, accurately predicting the creep life of chromium-molybdenum-vanadium steel is of great significance to ensure the safe operation of the equipment.

[0003] Traditional creep life prediction methods mainly rely on experimental data or empirical formulas. However, creep experiments are time-consuming and costly, making them difficult to carry out in practical applications. In addition, materials are affected by many factors during the creep process, such as temperature, stress, and microstructural changes, resulting in low accuracy of the prediction results. In recent years, with the development of data-driven methods, technologies such as machine learning have gradually been introduced into the field of material life prediction. Through the mining and analysis of large-scale data, the accuracy and generalization ability of predictions can be effectively improved. However, the data characteristics in the field of materials science are complex, and the problem of data scarcity still exists. How to improve the performance of the prediction model with limited data has become a technical problem that needs to be solved urgently.

[0004] The existing technology mainly has the following problems:

[0005] 1. When generating material data, traditional generative adversarial networks have difficulty capturing the hierarchical structure and complex geometric properties of material data. The generated data samples cannot maintain consistent quality at different scales, resulting in poor data expansion effects and unable to meet the needs of materials science for high-quality data.

[0006] 2. Most existing feature extraction models are based on fixed evolution rules and lack the ability to adapt to real-time changes in data characteristics, which results in the feature extraction process being inflexible and prone to problems such as gradient vanishing or gradient exploding, affecting the efficiency and stability of model training. At the same time, it is difficult for the model to effectively extract key features from complex material data, and its generalization ability is limited.

[0007] 3. Existing feature dimensionality reduction technologies often suffer from information loss when processing high-dimensional data, especially in the process of dimensionality reduction, it is difficult to effectively retain the core features of material data. Redundant features and noise are difficult to fully weaken, resulting in insufficient simplicity of features after dimensionality reduction, which affects the input quality and overall prediction accuracy of subsequent classifier models.

[0008] 4. Most existing classifier models rely on traditional activation functions and neural network structures, which cannot precisely control the flow of information. The activation function lacks the ability to dynamically adjust and is difficult to capture nonlinear changes in complex material data, resulting in insufficient accuracy in material life prediction and limited model processing capabilities.

[0009] Therefore, a data-driven creep life prediction method for chromium-molybdenum-vanadium steel is provided. Summary of the invention

[0010] The purpose of the present invention is to overcome the existing defects and provide a data-driven chromium-molybdenum-vanadium steel creep life prediction method, thereby improving the prediction ability of the material creep life.

[0011] The technical solution to achieve the above purpose is:

[0012] A data-driven creep life prediction method for chromium-molybdenum-vanadium steel, comprising:

[0013] Step S1, data collection, converting the collected data into a structured digital format and manually annotating the data;

[0014] Step S2, using a generative adversarial network algorithm based on hyperbolic space embedding to generate samples and then expand the data;

[0015] Step S3, using a neural network algorithm based on dynamic population evolution optimization as a feature extraction model, and training the feature extraction model on the expanded data;

[0016] Step S4, using an autoencoder neural network based on feature refinement as a feature dimension reduction model, and training the feature dimension reduction model on the data after feature extraction;

[0017] Step S5, using a fractional-order neural network based on quantum state transfer as a classifier model, and training the classifier model on the reduced-dimensional data;

[0018] Step S6, using the fully trained feature extraction model, feature dimension reduction model and classifier model to process and predict new samples, and then obtain a classification result for predicting the creep life of chromium-molybdenum-vanadium steel;

[0019] In step S2, the training process of the generative adversarial network algorithm based on hyperbolic space embedding is as follows:

[0020] Step S21, initialize the network parameters of the generator and the discriminator; the initialization method is expressed as:

[0021]

[0022] Where W crepresents the weight matrix of the generative adversarial network, b c represents the bias vector of the generative adversarial network; ~ represents compliance with a specific distribution, represents a normal distribution, represents a normal distribution with a mean of 0 and a variance of 1;

[0023] Step S22, mapping the original data into a hyperbolic space, and retaining the inherent hierarchical structure and complex characteristics of the data through a mapping function; assuming that the hyperbolic space is The nonlinear transformation is performed using the hyperbolic tangent function, expressed as:

[0024]

[0025] Where, X c is the original data vector, represents the i-th component of the original data vector, α c is an extended parameter used to adjust the sensitivity of embedding, d new is the embedding dimension, represents the data vector in the mapped hyperbolic space, The first parameter for adjusting the mapping intensity is used to control the distribution density of data in the hyperbolic space. A second parameter for adjusting the mapping strength is used to control the structure of the data in the hyperbolic space;

[0026] A dynamic adjustment mechanism is used so that the embedding dimension can be adjusted according to the real-time characteristics of the data. The calculation method is expressed as:

[0027] d new =δ c (X c ,θ d ) = d base +round(θ d ·(var(X c )+skew(X c )));

[0028] In the formula, δ c () is the dynamic dimension adjustment function, θ d is the adjustment coefficient, which is used to control the sensitivity of dimension adjustment, d base is the base embedding dimension, var(X c ) and skew(X c ) represent the variance and skewness of the data, respectively, and are used to reflect the distribution characteristics of the data;

[0029] Step S23, in the hyperbolic space, the generator creates new data samples by optimizing the loss function, and generates false data that is difficult for the discriminator to distinguish through the generator; the loss function of the generator is expressed as:

[0030]

[0031] In the formula, G c is the generator, D c is the discriminator, G c () is the generator function, represents the generated fake data, D c () is the discriminator function, z c is the input noise distribution, represents the loss function of the generator, φ c () is a composite function, θ c are the parameters of the generator, represents the regularization term of conditional dependence, C c is the conditional variable, i.e., the data variable;

[0032] The calculation method of the conditionally dependent regularization term is expressed as:

[0033]

[0034] In the formula, λ r is the regularization coefficient, which is used to control the influence of the regularization term. ∥∥ is the L2 norm. express expectations;

[0035] The composite function combines generated data and parameters to increase the complexity and adjustability of the data generation process. The calculation method is expressed as:

[0036]

[0037] Where Sig() is the Sigmoid activation function, μ c It is a regulation term that increases the model’s sensitivity to noise;

[0038] Step S24, improve the accuracy of the generator in identifying true and false samples by training the discriminator; the loss function of the discriminator is expressed as:

[0039]

[0040] In the formula, Denotes the loss function of the discriminator, D c (X c ) represents the evaluation of the discriminator on the real data, X c represents the real sample, D c (G c (z c )) represents the evaluation of the discriminator on the generated data, ψ c () is the discriminant result processing function, γ cTo control the slope of the function, it is used to enhance the model's ability to classify true and false data;

[0041] The discrimination result processing function uses the slope parameter of the control function to adjust the discrimination sensitivity. The calculation method is expressed as:

[0042]

[0043] In the formula, Dcs represents the discriminant result, that is, D c (G c (z c ));

[0044] Step S25, while generating data in each step, the generated data is optimized at multiple scales through the feature pyramid structure to capture data features at different scales; that is, the optimized data is represented as:

[0045]

[0046] Where P c () is the feature pyramid function, λ k is the layer weight, conv k () is the basic convolution operation of the kth layer, conv' k () is the auxiliary convolution operation of this layer, which is used to capture additional detail features. is the optimized generated data, Kcs is the number of convolutional layers, ξ k Parameters to control the contribution of auxiliary convolution;

[0047] Step S26, in the iterative process, optimize the generator and the discriminator until convergence; the parameter optimization method is expressed as:

[0048]

[0049] Where η c is the learning rate of the generative adversarial network, and are the gradients of weight and bias respectively, is the total loss function, including the sum of the losses of the generator and the discriminator, is the weight of the generative adversarial network before updating, is the bias of the generative adversarial network before updating, is the updated weight of the generative adversarial network, is the bias of the updated generative adversarial network;

[0050] Step S27, repeatedly iterating the steps S21 to S26 until a preset stop iteration condition is met, indicating that the training of the generative adversarial network algorithm based on hyperbolic space embedding is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times;

[0051] In step S5, the fractional-order neural network training process based on quantum state transfer is as follows:

[0052] Step S51, initialize the fractional-order neural network, define the neurons of the fractional-order neural network as quantum neurons, and set parameters for each quantum neuron in the fractional-order neural network; wherein the parameters include the angle and phase of the quantum gate, and the neural network weight and bias, and the initialization process is expressed as:

[0053]

[0054] In the formula, represents uniform distribution, θ u and φ u are the angle and phase of the quantum gate, evenly distributed in [0,2π], W u and b u are the weights and biases of the fractional-order neural network, following a normal distribution with a mean of 0 and a standard deviation of 1;

[0055] Step S52, in the forward propagation stage, the input feature-reduced data is first encoded into quantum bits, and each quantum bit is operated through a quantum gate to achieve non-classical information processing; the calculation method of the quantum gate conversion function is expressed as:

[0056]

[0057] In the formula, G u () represents quantum gate operation, i * is the imaginary unit, X u is the data input to the fractional-order neural network, that is, the data after feature dimension reduction, θ u and φ u Adjust the angle and phase of the door so that X u undergoes transformations in the complex number domain to simulate superposition effects of quantum states;

[0058] Step S53, using a fractional-order activation function, whose order is dynamically adjusted according to the error in the training process, to optimize the learning process and enhance the nonlinear expression ability of the model;

[0059] Specifically, let the fractional derivative be where α uIt is a dynamically adjusted fractional-order parameter that enables the activation function to respond more flexibly to changes in the input. The obtained fractional-order derivative can not only reflect local changes, but also capture the overall trend. It is suitable for processing highly nonlinear data. The calculation method is expressed as:

[0060]

[0061] Where Sig() is the Sigmoid activation function, Indicates f u The fractional derivative of u is the fractional order parameter, m is α u The next integer of the integer part of , Γ is the gamma function, used for normalization, Sig (m) () is the m-th order derivative of the Sigmoid activation function;

[0062] Step S54, using the gradient information of the quantum state to calculate the derivative of the loss function, and using the fractional-order derivative to update the weights and biases of the network; the update increment of the fractional-order neural network parameters is expressed as:

[0063]

[0064] In the formula, ΔW u is the weight update increment of the fractional-order neural network, Δb u is the bias update increment of the fractional-order neural network, η u is the learning rate of the fractional-order neural network, is the loss function of the fractional-order neural network. Use cross entropy loss function;

[0065] In the gradient calculation process of the loss function in back propagation, the quantum gradient is enhanced by the quantum state interference effect, and the gradient calculation method of the loss function with respect to the weight is expressed as:

[0066]

[0067] In the formula, represents the quantum gradient enhancement term, which enhances or weakens the gradient effect through the phase interference of quantum states, and ⊙ represents the Hadamard product;

[0068] Step S55, repeating forward propagation and backward propagation, in each iteration, updating the weight and bias parameters of the fractional-order neural network; expressed as:

[0069]

[0070] In the formula, represents the weight of the fractional-order neural network at the tth iteration, represents the bias of the fractional-order neural network at the tth iteration, represents the weight of the fractional-order neural network at the t+1th iteration, represents the bias of the fractional-order neural network at the t+1th iteration, and R() is the recoding function;

[0071] The recoding function is a composite function of adaptive learning rate adjustment and parameter range restriction, and the calculation method is expressed as:

[0072] R(p,Δp,η)=p+η0·f(Δp,p,η);

[0073] Where f() is a modulation function, which is used to adjust the learning rate η of the recoding function according to the current parameter p, the update amount Δp and the feedback of the learning environment, and η0 is the initial learning rate of the recoding function;

[0074] Based on the gradient amplitude of the parameters and the previous update history, the modulation function is calculated as:

[0075]

[0076] In the formula, g() is the adjustment function of the learning rate, increasing or decreasing the learning rate, depending on the past update efficiency, ∈ is a small constant to prevent division by zero;

[0077] After each iteration, according to the model's training accuracy P u , dynamically adjust g(η), expressed as:

[0078] g(η)=η·exp(-γ·(1-P u ));

[0079] Where γ is the sensitivity hyperparameter that controls the learning rate adjustment;

[0080] Step S56, repeatedly iterate the steps S51 to S55 until a preset stop iteration condition is met, which means that the fractional-order neural network training based on quantum state transfer is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0081] Preferably, in step S1, the data collection channels include laboratory creep experiments, published material databases, finite element simulation results, and microstructure characterization, thereby obtaining data, wherein:

[0082] Laboratory creep experiments record creep life and material behavior by subjecting material specimens to varying temperatures and stresses under controlled conditions;

[0083] Publicly available material databases are used to provide detailed data from historical creep experiments;

[0084] Finite element simulation results are used to generate data points that are not actually tested under specific conditions;

[0085] Microstructural characterization is used to describe in detail the internal structural changes of materials by using scanning electron microscopy, electron backscatter diffraction or transmission electron microscopy techniques;

[0086] The attributes of the data, that is, the data variables include: stress strain rate R a , Temperature D a , test duration T a , pre-strain variable P a , material composition ratio M a , grain size G a , dislocation density B a , creep void number H a , Residual life F a and organizational evolution index L a .

[0087] Preferably, in step S1, the structured digital format includes CSV and HDF5 formats.

[0088] Preferably, in step S1, the manually labeled categories include three categories: remaining life ≤ 25%, remaining life 25%-75% and remaining life ≥ 75%.

[0089] Preferably, in step S3, the training process of the neural network algorithm based on dynamic population evolution optimization is as follows:

[0090] Step S31, according to the bionic algorithm initialization method, in the initialization stage, an initial population is generated; each individual represents a configuration of network weights, and the population size is set to N p , initialize the weights and biases for the i-th individual, expressed as:

[0091]

[0092] Where W pi is the weight of the neural network corresponding to the i-th individual, b pi is the bias of the neural network corresponding to the i-th individual, represents the weight matrix of the i-th individual in the initial state, represents the bias of the i-th individual in the initial state, σ 2 represents the initialization variance, Indicates that the mean is 0 and the variance is σ 2 Normal distribution of is a normal distribution;

[0093] Step S32, for each individual in the population, use its corresponding neural network configuration to process the input training data, calculate the output of the model, and evaluate its performance according to a predetermined loss function; specifically, for the i-th individual, use its weight and bias to calculate the loss on the training data set, expressed as:

[0094]

[0095] Where, L pi is the loss of the neural network corresponding to the i-th individual, mps represents the number of samples input in the current batch, l() represents the composite loss function, and fsig() represents the neural network model function. represents the characteristics of the jth sample, represents the label of the jth sample;

[0096] The composite loss function contains a regularization term, which is calculated as follows:

[0097]

[0098] Where MSE() is the mean square error function, Reg(W pi ) is the regularization term, λ ps is the regularization parameter;

[0099] The calculation method of the regularization term is expressed as:

[0100]

[0101] Where W pi,k is the kth weight of the neural network corresponding to the i-th individual;

[0102] Step S33, according to the fitness of the individual, select the best performing individual from the current population to be retained as the candidate solution for the next generation; specifically, based on the fitness of the individual, the excellent individual has a higher probability of being selected, and the calculation method of the probability of the individual being selected is expressed as:

[0103]

[0104] Where P select (i) represents the probability of the i-th individual being selected, γ pse is the parameter controlling the selection pressure, L pk is the loss of the neural network corresponding to the kth individual;

[0105] Step S34, generate new individuals through crossover and mutation operations. The crossover operation allows two excellent individuals to exchange some genes to generate new offspring. Specifically, the crossover operation randomly selects two individuals for gene exchange, which is expressed as:

[0106] W′ pi =α pcs W p1 +(1-α pcs )W p2 ;

[0107] b' pi =α pcs b p1 +(1-α pcs )b p2 ;

[0108] In the formula, α pcs is the crossover rate, W p1 is the weight of the neural network corresponding to the first individual selected, b p1 is the bias of the neural network corresponding to the first individual selected, W p2 is the weight of the neural network corresponding to the second individual selected, b p2 is the bias of the neural network corresponding to the second individual selected, W' pi is the weight of the neural network corresponding to the individual after the crossover operation, b' pi is the bias of the neural network corresponding to the individual after the crossover operation;

[0109] The mutation operation performs a small random perturbation on the newly generated individual weights, expressed as:

[0110]

[0111] In the formula, τ 2 represents the variance of variation, W″ pi is the weight of the neural network corresponding to the individual after the mutation operation, b″ pi is the bias of the neural network corresponding to the individual after the mutation operation;

[0112] Step S35, repeatedly iterate the steps S31-S34 until the preset stop iteration condition is met, which means that the training of the neural network algorithm based on dynamic population evolution optimization is completed; wherein, the preset stop iteration condition is reaching the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0113] Preferably, in step S4, the training process of the autoencoder neural network based on feature refinement is as follows:

[0114] Step S41: The encoder uses a multi-layer nonlinear mapping structure to map high-dimensional data to an initial low-dimensional feature space; assuming that the data input to the autoencoder neural network is X r , then mapping the high-dimensional data to the initial low-dimensional feature space is expressed as:

[0115] Zr =Sig enc (W r X r +b r );

[0116] In the formula, Z r is the initial low-dimensional feature, W r is the weight matrix of the encoder, b r is the bias vector of the encoder, Sig enc () is the multi-layer Sigmoid activation function of the encoder;

[0117] Step S42, after the low-dimensional features are generated, the feature adjustment module automatically generates feature weights according to the feature importance in the current feature space; the calculation method of the initial weight matrix is ​​expressed as:

[0118] A r =diag(α r );

[0119] In the formula, A r is the initial weight matrix, α r is the feature weight vector, α r Each element α in r,i Initialized to the same value, indicating that all features have the same importance in the initial stage; diag() is a function for extracting the diagonal elements of the matrix;

[0120] The adjusted features can be expressed as:

[0121] Z′ r =A r Z r ;

[0122] In the formula, Z' r is the feature representation after feature weight adjustment;

[0123] In step S43, the feature adjustment module recursively optimizes the initially generated low-dimensional features, that is, in each round of iteration, the feature adjustment module adjusts the weights of each feature according to the performance of the features in the previous round, gradually enhances the features with important influence, and gradually weakens the redundant or noise features; suppose that in the tth round of iteration, the weight update rule is as follows:

[0124]

[0125] In the formula, represents the feature weight of the t+1th iteration, represents the feature weight of the tth iteration, η r is the learning rate of the autoencoder neural network, L r () is the loss function of the autoencoder neural network, Yr is the label data, Represents the gradient of the loss function with respect to the feature weight;

[0126] Step S44, the decoder remaps the low-dimensional features back to the high-dimensional space; the reconstruction process of the decoder is expressed as:

[0127] X' r =Sig dec (W' r Z' r +b' r );

[0128] Where X' r is the reconstructed high-dimensional data, W' r is the weight matrix of the decoder, b' r is the bias vector of the decoder, Sig dec () is the multi-layer Sigmoid activation function of the decoder;

[0129] Step S45, repeatedly iterate the steps S41 to S44 until a preset stop iteration condition is met, indicating that the training of the autoencoder neural network based on feature refinement is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0130] Preferably, in step S6, the collected raw data is input into a trained feature extraction and feature dimension reduction model for feature processing, and the processed features are input into a classifier model for classifier training to obtain classification results; wherein the classification categories include: remaining life ≤ 25%, remaining life 25%-75% and remaining life ≥ 75%.

[0131] The beneficial effects of the present invention are:

[0132] 1) Data augmentation is achieved through a generative adversarial network based on hyperbolic space embedding, which solves the problem of insufficient model generalization ability due to the scarcity of materials science data. The use of hyperbolic space embedding enables the generative adversarial network to more naturally capture the complex hierarchical structure and irregular geometric characteristics in material data. In the generation process, the generative adversarial network combines the multi-scale optimization of the feature pyramid, so that the generated samples have high quality at different scales, further improving the richness and diversity of the data.

[0133] 2) In the feature extraction process, a neural network algorithm based on dynamic population evolution optimization is adopted, and the evolution rules are automatically adjusted according to the data characteristics through a self-correction mechanism; the model can adjust the crossover and mutation operations according to the dynamic changes of the current data to ensure the flexibility of feature extraction, and solve the problem that traditional neural networks are susceptible to gradient disappearance and gradient explosion; through this adaptive mechanism, the model can more efficiently extract effective features from complex material data, enhancing the stability and efficiency of feature extraction;

[0134] 3) The autoencoder neural network based on feature refinement is used to reduce the dimensionality of the extracted high-dimensional features; the autoencoder maps the high-dimensional data to the low-dimensional feature space through multi-layer nonlinear mapping, and at the same time adds a feature adjustment module, and dynamically adjusts the feature space after dimensionality reduction through recursive feature adaptive optimization, so that important features can be strengthened and secondary or redundant features are gradually weakened, ensuring that the features after dimensionality reduction can retain data information and have good simplicity;

[0135] 4) In the classification stage, a fractional-order neural network based on quantum state transfer is used. The quantum state transfer mechanism can make the information flow and processing process in the neural network more accurate; the fractional-order activation function dynamically adjusts the order of the activation function according to the error in the training process, so that the model can more flexibly capture the nonlinear changes of complex material data, thereby improving the accuracy of life prediction; through the gradient enhancement of the quantum state, the model further optimizes the learning efficiency during the training process and improves the ability to predict the creep life of the material. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Figure 1 It is a flow chart of a data-driven method for predicting creep life of chromium-molybdenum-vanadium steel according to the present invention;

[0137] Figure 2 It is a training flow chart of the generative adversarial network algorithm based on hyperbolic space embedding in the present invention;

[0138] Figure 3 It is a training flow chart of a neural network algorithm based on dynamic population evolution optimization in the present invention;

[0139] Figure 4 It is a training flow chart of the autoencoder neural network based on feature refinement in the present invention;

[0140] Figure 5 It is a training flow chart of the fractional-order neural network based on quantum state transfer in the present invention. DETAILED DESCRIPTION

[0141] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0142] The present invention will be further described below in conjunction with the accompanying drawings.

[0143] like Figure 1 As shown, a data-driven creep life prediction method for chromium-molybdenum-vanadium steel includes:

[0144] Step S1, data collection, converting the collected data into a structured digital format and manually annotating the data.

[0145] In the embodiment, the data collection channels include laboratory creep experiments, published material databases, finite element simulation results, and microstructure characterization to obtain data, wherein:

[0146] Laboratory creep experiments record creep life and material behavior by subjecting material specimens to varying temperatures and stresses under controlled conditions;

[0147] Publicly available material databases are used to provide detailed data from historical creep experiments;

[0148] Finite element simulation results are used to generate data points that are not actually tested under specific conditions;

[0149] Microstructural characterization is used to describe in detail the internal structural changes of materials by using scanning electron microscopy, electron backscatter diffraction or transmission electron microscopy techniques;

[0150] The attributes of the data, that is, the data variables include: stress strain rate R a , Temperature D a , test duration T a , pre-strain variable P a , material composition ratio M a , grain size G a , dislocation density B a , creep void number H a , Residual life F a and organizational evolution index L aIt should be noted that this embodiment is only used to illustrate a data format and type of the present invention. In actual applications, the attributes of data are usually more than 10 attributes, and the number of data attributes may reach dozens.

[0151] In an embodiment, the structured digital formats include CSV and HDF5 formats to facilitate processing and analysis.

[0152] In the embodiment, the manually labeled categories include: remaining life ≤ 25%, remaining life 25%-75%, and remaining life ≥ 75%.

[0153] Step S2, using a generative adversarial network algorithm based on hyperbolic space embedding to generate samples and then expand the data.

[0154] In the present invention, the collection, acquisition, labeling and preprocessing of training data are time-consuming and labor-intensive, and insufficient training samples can easily lead to poor generalization ability of the model, while affecting the accuracy of the model; the present invention adopts a generative adversarial network algorithm based on hyperbolic space embedding to generate samples, thereby achieving data expansion; on the basis of the traditional generative adversarial network, the present invention embeds the operations of the generator and the discriminator into the hyperbolic space. Since the hyperbolic space can more naturally represent the hierarchical structure and irregular geometric characteristics of the data, it is particularly suitable for material science data with complex physical properties and organizational structure characteristics.

[0155] like Figure 2 As shown in Figure 1, the training process of the generative adversarial network algorithm based on hyperbolic space embedding is as follows:

[0156] Step S21, initialize the network parameters of the generator and the discriminator; the initialization method is expressed as:

[0157]

[0158] Where W c represents the weight matrix of the generative adversarial network, b c represents the bias vector of the generative adversarial network; ~ represents compliance with a specific distribution, represents a normal distribution, represents a normal distribution with a mean of 0 and a variance of 1;

[0159] Step S22, mapping the original data into a hyperbolic space, and retaining the inherent hierarchical structure and complex characteristics of the data through a mapping function; assuming that the hyperbolic space is The nonlinear transformation is performed using the hyperbolic tangent function, expressed as:

[0160]

[0161] Where, X c is the original data vector, Xci represents the i-th component of the original data vector, α c is an extended parameter used to adjust the sensitivity of embedding, d new is the embedding dimension, represents the data vector in the hyperbolic space after mapping, α ci is the first parameter for adjusting the mapping intensity, which is used to control the distribution density of data in the hyperbolic space. ci A second parameter for adjusting the mapping strength is used to control the structure of the data in the hyperbolic space;

[0162] In one embodiment, in the field of materials science, the complexity of data can vary greatly due to different experimental conditions and sample differences. Therefore, a dynamic adjustment mechanism is used so that the embedding dimension can be adjusted according to the real-time characteristics of the data, thereby improving the flexibility and accuracy of data representation. The calculation method is expressed as:

[0163] d new =δ c (X c ,θ d ) = d base +round(θ d ·(var(X c )+skew(X c )));

[0164] In the formula, δ c () is the dynamic dimension adjustment function, θ d is the adjustment coefficient, which is used to control the sensitivity of dimension adjustment, d base is the base embedding dimension, var(X c ) and skew(X c ) represent the variance and skewness of the data, respectively, and are used to reflect the distribution characteristics of the data. base Set to 3, θ d Set to 2;

[0165] Step S23, in the hyperbolic space, the generator creates new data samples by optimizing the loss function, and generates false data that is difficult for the discriminator to distinguish through the generator; the loss function of the generator is expressed as:

[0166]

[0167] In the formula, G c is the generator, D c is the discriminator, G c () is the generator function, represents the generated fake data, D c () is the discriminator function, z c is the input noise distribution, represents the loss function of the generator, φ c () is a composite function, θ c are the parameters of the generator, represents the regularization term of conditional dependence, C c is the conditional variable, i.e., the data variable;

[0168] The calculation method of the conditionally dependent regularization term is expressed as:

[0169]

[0170] In the formula, λ r is the regularization coefficient, set to 0.2, which is used to control the influence of the regularization term. ∥∥ is the L2 norm. express expectations;

[0171] The composite function combines generated data and parameters to increase the complexity and adjustability of the data generation process. The calculation method is expressed as:

[0172]

[0173] Where Sig() is the Sigmoid activation function, μ c It is a regulation item that increases the model's sensitivity to noise and is set to 0.1;

[0174] Step S24, improve the accuracy of the generator in identifying true and false samples by training the discriminator; the loss function of the discriminator is expressed as:

[0175]

[0176] In the formula, Denotes the loss function of the discriminator, D c (X c ) represents the evaluation of the discriminator on the real data, X c represents the real sample, D c (G c (z c )) represents the evaluation of the discriminator on the generated data, ψ c () is the discriminant result processing function, γ c To control the slope of the function, it is used to enhance the model's ability to classify true and false data;

[0177] The discrimination result processing function uses the slope parameter of the control function to adjust the discrimination sensitivity. The calculation method is expressed as:

[0178]

[0179] In the formula, Dcs represents the discriminant result, that is, D c(G c (z c )), γ c Set to 1;

[0180] Step S25, while generating data at each step, the generated data is optimized at multiple scales through the feature pyramid structure to ensure that the generated data has high quality at different scales and captures data features at different scales; that is, the optimized data is represented as:

[0181]

[0182] Where P c () is the feature pyramid function, λ k is the layer weight, conv k () is the basic convolution operation of the kth layer, conv' k () is the auxiliary convolution operation of this layer, which is used to capture additional detail features. is the optimized generated data, Kcs is the number of convolutional layers, ξ k Parameters to control the contribution of auxiliary convolution;

[0183] Step S26, in the iterative process, optimize the generator and the discriminator until convergence; the parameter optimization method is expressed as:

[0184]

[0185] Where η c is the learning rate of the generated adversarial network, set to 0.01, and are the gradients of weight and bias respectively, is the total loss function, including the sum of the losses of the generator and the discriminator, is the weight of the generative adversarial network before updating, is the bias of the generative adversarial network before updating, is the updated weight of the generative adversarial network, is the bias of the updated generative adversarial network;

[0186] Step S27, repeat the iteration steps S21-S26 until the preset stop iteration condition is met, which means that the training of the generative adversarial network algorithm based on hyperbolic space embedding is completed; wherein the preset stop iteration condition is to reach the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0187] Step S3, using a neural network algorithm based on dynamic population evolution optimization as a feature extraction model, and training the feature extraction model on the expanded data.

[0188] The present invention adopts a 6-layer fully connected neural network for feature extraction. In the prior art, some solutions use neural networks for feature extraction. In some neural network structures, problems such as gradient vanishing, gradient explosion or falling into local optimal solutions may be encountered, affecting the stability of training and the performance of the model. The present invention adopts a neural network algorithm based on dynamic population evolution optimization as a feature extraction model. In traditional population evolution algorithms, the evolution of all individuals is based on fixed rules. The present invention uses a self-correction mechanism to automatically adjust the evolution rules according to the characteristics of the current training data, and adjusts the probability of crossover and mutation according to the changing trend of the loss function, so that the algorithm can adapt to different data distributions more flexibly and improve the generalization ability and training efficiency of the model.

[0189] like Figure 3 As shown in the figure, the training process of the neural network algorithm based on dynamic population evolution optimization is as follows:

[0190] Step S31, according to the bionic algorithm initialization method, in the initialization stage, an initial population is generated; each individual represents a configuration of network weights, and the population size is set to N p , initialize the weights and biases for the i-th individual, expressed as:

[0191]

[0192] Where W pi is the weight of the neural network corresponding to the i-th individual, b pi is the bias of the neural network corresponding to the i-th individual, represents the weight matrix of the i-th individual in the initial state, represents the bias of the i-th individual in the initial state, σ 2 represents the initialization variance, which is set to 0.01. Indicates that the mean is 0 and the variance is σ 2 Normal distribution of is a normal distribution;

[0193] Step S32, for each individual in the population, use its corresponding neural network configuration to process the input training data, calculate the output of the model, and evaluate its performance according to a predetermined loss function; specifically, for the i-th individual, use its weight and bias to calculate the loss on the training data set, expressed as:

[0194]

[0195] Where, L pi is the loss of the neural network corresponding to the i-th individual, mps represents the number of samples input in the current batch, l() represents the composite loss function, and fsig() represents the neural network model function. represents the characteristics of the jth sample, represents the label of the jth sample;

[0196] The composite loss function contains a regularization term, which can increase the generalization ability of the model. The calculation method is expressed as:

[0197]

[0198] Where MSE() is the mean square error function, Reg(W pi ) is the regularization term, λ ps is the regularization parameter;

[0199] The calculation method of the regularization term is expressed as:

[0200]

[0201] Where W pi,k is the kth weight of the neural network corresponding to the i-th individual;

[0202] Step S33, according to the fitness of the individual, select the best performing individual from the current population to be retained as the candidate solution for the next generation; specifically, based on the fitness of the individual, the excellent individual has a higher probability of being selected, and the calculation method of the probability of the individual being selected is expressed as:

[0203]

[0204] Where P select (i) represents the probability of the i-th individual being selected, γ pse is the parameter that controls the selection pressure, set to 2, L pk is the loss of the neural network corresponding to the kth individual;

[0205] Step S34, generate new individuals through crossover and mutation operations. The crossover operation allows two excellent individuals to exchange some genes to generate new offspring, and the mutation operation randomly changes some genes in the individual to increase the diversity of the population. Specifically, the crossover operation randomly selects two individuals for gene exchange, which is expressed as:

[0206] W′ pi =α pcs W p1 +(1-α pcs )W p2 ;

[0207] b' pi =α pcs b p1 +(1-α pcs )b p2 ;

[0208] In the formula, α pcs is the crossover rate, set to 0.3, W p1 is the weight of the neural network corresponding to the first individual selected, b p1 is the bias of the neural network corresponding to the first individual selected, W p2 is the weight of the neural network corresponding to the second individual selected, b p2 is the bias of the neural network corresponding to the second individual selected, W' pi is the weight of the neural network corresponding to the individual after the crossover operation, b' pi is the bias of the neural network corresponding to the individual after the crossover operation;

[0209] The mutation operation performs a small random perturbation on the newly generated individual weights, expressed as:

[0210]

[0211] In the formula, τ 2 represents the variance of the variation, set to 0.04, W″ pi is the weight of the neural network corresponding to the individual after the mutation operation, b″ pi is the bias of the neural network corresponding to the individual after the mutation operation;

[0212] Step S35, repeat the iteration steps S31-S34 until the preset stop iteration condition is met, which means that the training of the neural network algorithm based on dynamic population evolution optimization is completed; wherein, the preset stop iteration condition is reaching the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0213] Step S4, using an autoencoder neural network based on feature refinement as a feature dimensionality reduction model, and training the feature dimensionality reduction model on the data after feature extraction.

[0214] The present invention adopts an autoencoding neural network based on feature refinement as a dimensionality reduction model. The autoencoding neural network based on feature refinement consists of three parts: an encoder, a decoder, and a feature adjustment module. The encoder is responsible for mapping high-dimensional input data to a low-dimensional feature space, and the decoder is used to reconstruct the reduced-dimensional features back to the original space to ensure the reversibility of the dimensionality reduction process; the feature adjustment module dynamically adjusts the reduced-dimensional feature space through recursive feature adaptive optimization, so that important features can be strengthened and secondary features are gradually weakened, so that the reduced-dimensional feature representation has better simplicity while retaining data information.

[0215] like Figure 4 As shown in the figure, the training process of the autoencoder neural network based on feature refinement is as follows:

[0216] Step S41: The encoder uses a multi-layer nonlinear mapping structure to map high-dimensional data to an initial low-dimensional feature space; assuming that the data input to the autoencoder neural network is X r , then mapping the high-dimensional data to the initial low-dimensional feature space is expressed as:

[0217] Z r =Sig enc (W r X r +b r );

[0218] In the formula, Z r is the initial low-dimensional feature, W r is the weight matrix of the encoder, b r is the bias vector of the encoder, Sig enc () is the multi-layer Sigmoid activation function of the encoder;

[0219] Step S42, after the low-dimensional features are generated, the feature adjustment module automatically generates feature weights according to the feature importance in the current feature space. When the module is initialized, the same initial weights are assigned to all features so that they can be gradually adjusted according to feature contributions in subsequent steps. The calculation method of the initial weight matrix is ​​expressed as:

[0220] A r =diag(α r );

[0221] In the formula, A r is the initial weight matrix, α r is the feature weight vector, α r Each element α in r,i Initialized to the same value, indicating that all features have the same importance in the initial stage; diag() is a function for extracting the diagonal elements of the matrix;

[0222] The adjusted features can be expressed as:

[0223] Z′ r =A r Z r ;

[0224] In the formula, Z' r is the feature representation after feature weight adjustment;

[0225] In step S43, the feature adjustment module recursively optimizes the initially generated low-dimensional features, that is, in each round of iteration, the feature adjustment module adjusts the weights of each feature according to the performance of the features in the previous round, gradually enhances the features with important influence, and gradually weakens the redundant or noise features; suppose that in the tth round of iteration, the weight update rule is as follows:

[0226]

[0227] In the formula, represents the feature weight of the t+1th iteration,

[0228] represents the feature weight of the tth iteration, η r is the learning rate of the autoencoder neural network, set to 0.01, L r () is the loss function of the autoencoder neural network, Y r is the label data, Represents the gradient of the loss function with respect to the feature weight;

[0229] Step S44, in order to ensure the effectiveness of the dimensionality reduction process, the decoder remaps the low-dimensional features back to the high-dimensional space to ensure that the dimensionality reduction process does not lose important information; the reconstruction process of the decoder is expressed as:

[0230] X' r =Sig dec (W' r Z' r +b' r );

[0231] Where X' r is the reconstructed high-dimensional data, W' r is the weight matrix of the decoder, b' r is the bias vector of the decoder, Sig dec () is the multi-layer Sigmoid activation function of the decoder;

[0232] Step S45, repeat the iteration steps S41-S44 until the preset stop iteration condition is met, which means that the training of the autoencoder neural network based on feature refinement is completed; wherein the preset stop iteration condition is reaching the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0233] Step S5, using a fractional-order neural network based on quantum state transfer as a classifier model, and training the classifier model on the reduced-dimensional data.

[0234] The reduced-dimensional data is input into the classifier to train the classifier model. The present invention adopts a fractional-order neural network based on quantum state transfer as the classification algorithm. On the basis of the traditional fractional-order neural network, a quantum state transfer mechanism is adopted, so that the network can more finely regulate the flow and processing of information with the help of quantum bit states, thereby improving the accuracy of life prediction.

[0235] like Figure 5 As shown in the figure, the training process of the fractional-order neural network based on quantum state transfer is as follows:

[0236] Step S51, initialize the fractional-order neural network, define the neurons of the fractional-order neural network as quantum neurons, and set parameters for each quantum neuron in the fractional-order neural network; wherein the parameters include the angle and phase of the quantum gate, and the neural network weight and bias, and the initialization process is expressed as:

[0237]

[0238] In the formula, represents uniform distribution, θ u and φ u are the angle and phase of the quantum gate, evenly distributed in [0,2π], W u and b u are the weights and biases of the fractional-order neural network, following a normal distribution with a mean of 0 and a standard deviation of 1;

[0239] Step S52, in the forward propagation stage, the input feature-reduced data is first encoded into quantum bits, and each quantum bit is operated through a quantum gate to achieve non-classical information processing; the calculation method of the quantum gate conversion function is expressed as:

[0240]

[0241] In the formula, G u () represents quantum gate operation, i * is the imaginary unit, X u is the data input to the fractional-order neural network, that is, the data after feature dimension reduction, θ u and φ u Adjust the angle and phase of the door so that X u undergoes transformations in the complex number domain to simulate superposition effects of quantum states;

[0242] Step S53, using a fractional-order activation function, whose order is dynamically adjusted according to the error in the training process, to optimize the learning process and enhance the nonlinear expression ability of the model;

[0243] Specifically, let the fractional derivative be D u αu , where α u It is a dynamically adjusted fractional-order parameter that enables the activation function to respond more flexibly to changes in the input. The obtained fractional-order derivative can not only reflect local changes, but also capture the overall trend. It is suitable for processing highly nonlinear data. The calculation method is expressed as:

[0244]

[0245] Where Sig() is the Sigmoid activation function, Indicates f u The fractional derivative of u is the fractional order parameter, m is α u The next integer of the integer part of , Γ is the gamma function, used for normalization, Sig (m) () is the m-th order derivative of the Sigmoid activation function;

[0246] Step S54, using the gradient information of the quantum state to calculate the derivative of the loss function, and using the fractional-order derivatives to update the weights and biases of the network, to ensure that the fractional-order neural network can effectively learn the patterns in the data while maintaining the quantum properties; the update increment of the fractional-order neural network parameters is expressed as:

[0247]

[0248] In the formula, ΔW u is the weight update increment of the fractional-order neural network, Δb u is the bias update increment of the fractional-order neural network, η u is the learning rate of the fractional-order neural network, set to 0.1, is the loss function of the fractional-order neural network. Use cross entropy loss function;

[0249] In the gradient calculation process of the loss function in back propagation, the quantum gradient is enhanced by the quantum state interference effect, and the gradient calculation method of the loss function with respect to the weight is expressed as:

[0250]

[0251] In the formula, represents the quantum gradient enhancement term, which enhances or weakens the gradient effect through the phase interference of quantum states, and ⊙ represents the Hadamard product;

[0252] Step S55, repeating forward propagation and backward propagation, in each iteration, updating the weight and bias parameters of the fractional-order neural network; expressed as:

[0253]

[0254] In the formula, represents the weight of the fractional-order neural network at the tth iteration, represents the bias of the fractional-order neural network at the tth iteration, represents the weight of the fractional-order neural network at the t+1th iteration, represents the bias of the fractional-order neural network at the t+1th iteration, and R() is the recoding function;

[0255] The recoding function can achieve real-time adjustment of the parameter update strategy during the training process through the dynamic parameter recoding mechanism, improve the model's adaptability to changes in material behavior under different experimental conditions, and thus achieve higher prediction accuracy and stability in materials science applications. The recoding function is a composite function of adaptive learning rate adjustment and parameter range restriction, and the calculation method is expressed as:

[0256] R(p,Δp,η)=p+η0·f(Δp,p,η);

[0257] Where f() is a modulation function, which is used to adjust the learning rate η of the recoding function according to the current parameter p, the update amount Δp and the feedback of the learning environment. η0 is the initial learning rate of the recoding function, and η0 is set to 0.01.

[0258] Based on the gradient amplitude of the parameters and the previous update history, the modulation function is calculated as:

[0259]

[0260] Where g() is the adjustment function of the learning rate, increasing or decreasing the learning rate, depending on the past update efficiency, ∈ is a small constant to prevent division by zero, ∈ is set to 0.01;

[0261] After each iteration, according to the model's training accuracy P u , dynamically adjust g(η), expressed as:

[0262] g(η)=η·exp(-γ·(1-P u ));

[0263] Where γ is the sensitivity hyperparameter that controls the learning rate adjustment and is set to 0.5;

[0264] Step S56, repeat the iteration steps S51-S55 until the preset stop iteration condition is met, which means that the fractional-order neural network training based on quantum state transfer is completed; wherein the preset stop iteration condition is reaching the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

[0265] Step S6, using the fully trained feature extraction model, feature dimension reduction model and classifier model to process and predict new samples, and then obtain the classification result of predicting the creep life of chromium-molybdenum-vanadium steel.

[0266] In the embodiment, the collected raw data is input into the trained feature extraction and feature dimension reduction model for feature processing, and the processed features are input into the classifier model for classifier training to obtain classification results; wherein, the classification categories include: remaining life ≤ 25%, remaining life 25%-75% and remaining life ≥ 75%.

[0267] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data-driven creep life prediction method for chromium-molybdenum-vanadium steel, characterized in that: include: Step S1, data collection, converting the collected data into a structured digital format and manually annotating the data; Step S2, using a generative adversarial network algorithm based on hyperbolic space embedding to generate samples and then expand the data; Step S3, using a neural network algorithm based on dynamic population evolution optimization as a feature extraction model, and training the feature extraction model on the expanded data; Step S4, using an autoencoder neural network based on feature refinement as a feature dimension reduction model, and training the feature dimension reduction model on the data after feature extraction; Step S5, using a fractional-order neural network based on quantum state transfer as a classifier model, and training the classifier model on the reduced-dimensional data; Step S6, using the fully trained feature extraction model, feature dimension reduction model and classifier model to process and predict new samples, and then obtain a classification result for predicting the creep life of chromium-molybdenum-vanadium steel; In step S2, the training process of the generative adversarial network algorithm based on hyperbolic space embedding is as follows: Step S21, initialize the network parameters of the generator and the discriminator; the initialization method is expressed as: Where W c represents the weight matrix of the generative adversarial network, b c represents the bias vector of the generative adversarial network; ~ represents compliance with a specific distribution, represents a normal distribution, represents a normal distribution with a mean of 0 and a variance of 1; Step S22, mapping the original data into a hyperbolic space, and retaining the inherent hierarchical structure and complex characteristics of the data through a mapping function; assuming that the hyperbolic space is The nonlinear transformation is performed using the hyperbolic tangent function, expressed as: Where, X c is the original data vector, represents the i-th component of the original data vector, α c is an extended parameter used to adjust the sensitivity of embedding, d new is the embedding dimension, represents the data vector in the mapped hyperbolic space, The first parameter for adjusting the mapping intensity is used to control the distribution density of data in the hyperbolic space. A second parameter for adjusting the mapping strength is used to control the structure of the data in the hyperbolic space; A dynamic adjustment mechanism is used so that the embedding dimension can be adjusted according to the real-time characteristics of the data. The calculation method is expressed as: d new =d c (X c ,i d )=d base +round(θ d ·(var(X c )+skew(X c ))); In the formula, δ c () is the dynamic dimension adjustment function, θ d is the adjustment coefficient, which is used to control the sensitivity of dimension adjustment, d base is the base embedding dimension, var(X c ) and skew(X c ) represent the variance and skewness of the data, respectively, and are used to reflect the distribution characteristics of the data; Step S23, in the hyperbolic space, the generator creates new data samples by optimizing the loss function, and generates false data that is difficult for the discriminator to distinguish through the generator; the loss function of the generator is expressed as: In the formula, G c is the generator, D c is the discriminator, G c () is the generator function, represents the generated fake data, D c () is the discriminator function, z c is the input noise distribution, represents the loss function of the generator, φ c () is a composite function, θ c are the parameters of the generator, represents the regularization term of conditional dependence, C c is the conditional variable, i.e., the data variable; The calculation method of the conditionally dependent regularization term is expressed as: In the formula, λ r is the regularization coefficient, which is used to control the influence of the regularization term, || || is the L2 norm, express expectations; The composite function combines generated data and parameters to increase the complexity and adjustability of the data generation process. The calculation method is expressed as: Where Sig() is the Sigmoid activation function, μ c It is a regulation term that increases the model’s sensitivity to noise; Step S24, improve the accuracy of the generator in identifying true and false samples by training the discriminator; the loss function of the discriminator is expressed as: In the formula, Denotes the loss function of the discriminator, D c (X c ) represents the evaluation of the discriminator on the real data, X c represents the real sample, D c (G c (z c )) represents the evaluation of the discriminator on the generated data, ψ c () is the discriminant result processing function, γ c To control the slope of the function, it is used to enhance the model's ability to classify true and false data; The discrimination result processing function uses the slope parameter of the control function to adjust the discrimination sensitivity. The calculation method is expressed as: In the formula, Dcs represents the discriminant result, that is, D c (G c (z c )); Step S25, while generating data in each step, the generated data is optimized at multiple scales through the feature pyramid structure to capture data features at different scales; that is, the optimized data is represented as: Where P c () is the feature pyramid function, λ k is the layer weight, conv k () is the basic convolution operation of the kth layer, conv' k () is the auxiliary convolution operation of this layer, which is used to capture additional detail features. is the optimized generated data, Kcs is the number of convolutional layers, ξ k Parameters to control the contribution of auxiliary convolution; Step S26, in the iterative process, optimize the generator and the discriminator until convergence; the parameter optimization method is expressed as: Where η c is the learning rate of the generative adversarial network, and are the gradients of weight and bias respectively, is the total loss function, including the sum of the losses of the generator and the discriminator, is the weight of the generative adversarial network before updating, is the bias of the generative adversarial network before updating, is the updated weight of the generative adversarial network, is the bias of the updated generative adversarial network; Step S27, repeatedly iterating the steps S21 to S26 until a preset stop iteration condition is met, indicating that the training of the generative adversarial network algorithm based on hyperbolic space embedding is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times; In step S5, the fractional-order neural network training process based on quantum state transfer is as follows: Step S51, initialize the fractional-order neural network, define the neurons of the fractional-order neural network as quantum neurons, and set parameters for each quantum neuron in the fractional-order neural network; wherein the parameters include the angle and phase of the quantum gate, and the neural network weight and bias, and the initialization process is expressed as: In the formula, represents uniform distribution, θ u and φ u are the angle and phase of the quantum gate, evenly distributed in [0,2π], W u and b u are the weights and biases of the fractional-order neural network, following a normal distribution with a mean of 0 and a standard deviation of 1; Step S52, in the forward propagation stage, the input feature-reduced data is first encoded into quantum bits, and each quantum bit is operated through a quantum gate to achieve non-classical information processing; the calculation method of the quantum gate conversion function is expressed as: In the formula, G u () represents quantum gate operation, i * is the imaginary unit, X u is the data input to the fractional-order neural network, that is, the data after feature dimension reduction, θ u and φ u Adjust the angle and phase of the door so that X u undergoes transformations in the complex number domain to simulate superposition effects of quantum states; Step S53, using a fractional-order activation function, whose order is dynamically adjusted according to the error in the training process, to optimize the learning process and enhance the nonlinear expression ability of the model; Specifically, let the fractional derivative be where α u It is a dynamically adjusted fractional-order parameter that enables the activation function to respond more flexibly to changes in the input. The obtained fractional-order derivative can not only reflect local changes, but also capture the overall trend. It is suitable for processing highly nonlinear data. The calculation method is expressed as: Where Sig() is the Sigmoid activation function, Indicates f u The fractional derivative of u is the fractional order parameter, m is α u The next integer in the integer part of is the gamma function, used for normalization, Sig (m) () is the m-th order derivative of the Sigmoid activation function; Step S54, using the gradient information of the quantum state to calculate the derivative of the loss function, and using the fractional-order derivative to update the weights and biases of the network; the update increment of the fractional-order neural network parameters is expressed as: In the formula, ΔW u is the weight update increment of the fractional-order neural network, Δb u is the bias update increment of the fractional-order neural network, η u is the learning rate of the fractional-order neural network, is the loss function of the fractional-order neural network. Use cross entropy loss function; In the gradient calculation process of the loss function in back propagation, the quantum gradient is enhanced by the quantum state interference effect, and the gradient calculation method of the loss function with respect to the weight is expressed as: In the formula, Q(W u ) represents the quantum gradient enhancement term, which enhances or weakens the gradient effect through the phase interference of quantum states, and ⊙ represents the Hadamard product; Step S55, repeating forward propagation and backward propagation, in each iteration, updating the weight and bias parameters of the fractional-order neural network; expressed as: In the formula, represents the weight of the fractional-order neural network at the tth iteration, represents the bias of the fractional-order neural network at the tth iteration, represents the weight of the fractional-order neural network at the t+1th iteration, represents the bias of the fractional-order neural network at the t+1th iteration, and R() is the recoding function; The recoding function is a composite function of adaptive learning rate adjustment and parameter range restriction, and the calculation method is expressed as: R(p,Δp,η)=p+η0·f(Δp,p,η); Where f() is a modulation function, which is used to adjust the learning rate η of the recoding function according to the current parameter p, the update amount Δp and the feedback of the learning environment, and η0 is the initial learning rate of the recoding function; Based on the gradient amplitude of the parameters and the previous update history, the modulation function is calculated as: In the formula, g() is the adjustment function of the learning rate, increasing or decreasing the learning rate, depending on the past update efficiency, ∈ is a small constant to prevent division by zero; After each iteration, according to the model's training accuracy P u , dynamically adjust g(η), expressed as: g(η)=η·exp(-γ·(1-P u )); Where γ is the sensitivity hyperparameter that controls the learning rate adjustment; Step S56, repeatedly iterate the steps S51 to S55 until a preset stop iteration condition is met, which means that the fractional-order neural network training based on quantum state transfer is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

2. The data-driven creep life prediction method for chromium-molybdenum-vanadium steel according to claim 1 is characterized in that: In step S1, the data collection channels include laboratory creep experiments, published material databases, finite element simulation results, and microstructure characterization, thereby obtaining data, wherein: Laboratory creep experiments record creep life and material behavior by subjecting material specimens to varying temperatures and stresses under controlled conditions; Publicly available material databases are used to provide detailed data from historical creep experiments; Finite element simulation results are used to generate data points that are not actually tested under specific conditions; Microstructural characterization is used to describe in detail the internal structural changes of materials by using scanning electron microscopy, electron backscatter diffraction or transmission electron microscopy techniques; The attributes of the data, that is, the data variables include: stress strain rate R a , Temperature D a , test duration T a , pre-strain variable P a , material composition ratio M a , grain size G a , dislocation density B a , creep void number H a , Residual life F a and organizational evolution index L a .

3. The data-driven creep life prediction method for chromium-molybdenum-vanadium steel according to claim 1 is characterized in that: In the step S1, the structured digital format includes CSV and HDF5 formats.

4. The data-driven creep life prediction method for chromium-molybdenum-vanadium steel according to claim 1 is characterized in that: In the step S1, the manually labeled categories include three categories: remaining life ≤ 25%, remaining life 25%-75% and remaining life ≥ 75%.

5. The method for predicting creep life of chromium-molybdenum-vanadium steel based on data drive according to claim 1, characterized in that: In step S3, the training process of the neural network algorithm based on dynamic population evolution optimization is as follows: Step S31, according to the bionic algorithm initialization method, in the initialization stage, an initial population is generated; each individual represents a configuration of network weights, and the population size is set to N p , initialize the weights and biases for the i-th individual, expressed as: Where W pi is the weight of the neural network corresponding to the i-th individual, b pi is the bias of the neural network corresponding to the i-th individual, represents the weight matrix of the i-th individual in the initial state, represents the bias of the i-th individual in the initial state, σ 2 represents the initialization variance, Indicates that the mean is 0 and the variance is σ 2 Normal distribution of is a normal distribution; Step S32, for each individual in the population, use its corresponding neural network configuration to process the input training data, calculate the output of the model, and evaluate its performance according to a predetermined loss function; specifically, for the i-th individual, use its weight and bias to calculate the loss on the training data set, expressed as: Where, L pi is the loss of the neural network corresponding to the i-th individual, mps represents the number of samples input in the current batch, l( ) represents the composite loss function, fsig( ) represents the neural network model function, represents the characteristics of the jth sample, represents the label of the jth sample; The composite loss function contains a regularization term, which is calculated as follows: Where MSE() is the mean square error function, Reg(W pi ) is the regularization term, λ ps is the regularization parameter; The calculation method of the regularization term is expressed as: Where W pi,k is the kth weight of the neural network corresponding to the i-th individual; Step S33, according to the fitness of the individual, select the best performing individual from the current population to be retained as the candidate solution for the next generation; specifically, based on the fitness of the individual, the excellent individual has a higher probability of being selected, and the calculation method of the probability of the individual being selected is expressed as: Where P select (i) represents the probability of the i-th individual being selected, γ pse is the parameter controlling the selection pressure, L pk is the loss of the neural network corresponding to the kth individual; Step S34, generate new individuals through crossover and mutation operations. The crossover operation allows two excellent individuals to exchange some genes to generate new offspring. Specifically, the crossover operation randomly selects two individuals for gene exchange, which is expressed as: W' pi =a pcs W p1 +(1-a pcs )W p2 ; b' pi =a pcs b p1 +(1-a pcs )b p2 ; In the formula, α pcs is the crossover rate, W p1 is the weight of the neural network corresponding to the first individual selected, b p1 is the bias of the neural network corresponding to the first individual selected, W p2 is the weight of the neural network corresponding to the second individual selected, b p2 is the bias of the neural network corresponding to the second individual selected, W' pi is the weight of the neural network corresponding to the individual after the crossover operation, b' pi is the bias of the neural network corresponding to the individual after the crossover operation; The mutation operation performs a small random perturbation on the newly generated individual weights, expressed as: In the formula, τ 2 represents the variance of variation, W″ pi is the weight of the neural network corresponding to the individual after the mutation operation, b″ pi is the bias of the neural network corresponding to the individual after the mutation operation; Step S35, repeatedly iterate the steps S31-S34 until the preset stop iteration condition is met, which means that the training of the neural network algorithm based on dynamic population evolution optimization is completed; wherein, the preset stop iteration condition is reaching the preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

6. The method for predicting creep life of chromium-molybdenum-vanadium steel based on data drive according to claim 1, characterized in that: In step S4, the training process of the autoencoder neural network based on feature refinement is as follows: Step S41: The encoder uses a multi-layer nonlinear mapping structure to map high-dimensional data to an initial low-dimensional feature space; assuming that the data input to the autoencoder neural network is X r , then mapping the high-dimensional data to the initial low-dimensional feature space is expressed as: Z r =Sig enc (W r X r +b r ); In the formula, Z r is the initial low-dimensional feature, W r is the weight matrix of the encoder, b r is the encoder bias vector, Sig enc () is the multi-layer Sigmoid activation function of the encoder; Step S42, after the low-dimensional features are generated, the feature adjustment module automatically generates feature weights according to the feature importance in the current feature space; the calculation method of the initial weight matrix is ​​expressed as: A r =diag(a r ); In the formula, A r is the initial weight matrix, α r is the feature weight vector, α r Each element α in r,i Initialized to the same value, indicating that all features have the same importance in the initial stage; diag() is a function for extracting the diagonal elements of the matrix; The adjusted features can be expressed as: From r =A r From r ; In the formula, Z' r is the feature representation after feature weight adjustment; In step S43, the feature adjustment module recursively optimizes the initially generated low-dimensional features, that is, in each round of iteration, the feature adjustment module adjusts the weights of each feature according to the performance of the features in the previous round, gradually enhances the features with important influence, and gradually weakens the redundant or noise features; suppose that in the tth round of iteration, the weight update rule is as follows: In the formula, represents the feature weight of the t+1th iteration, represents the feature weight of the tth iteration, η r is the learning rate L of the autoencoder neural network r () is the loss function of the autoencoder neural network, Y r is the label data, Represents the gradient of the loss function with respect to the feature weight; Step S44, the decoder remaps the low-dimensional features back to the high-dimensional space; The reconstruction process of the decoder is expressed as: X' r =Sig dec (W' r Z' r +b' r ); Where X' r is the reconstructed high-dimensional data, W' r is the weight matrix of the decoder, b' r is the bias vector of the decoder, Sig dec () is the multi-layer Sigmoid activation function of the decoder; Step S45, repeatedly iterate the steps S41 to S44 until a preset stop iteration condition is met, indicating that the training of the autoencoder neural network based on feature refinement is completed; wherein the preset stop iteration condition is reaching a preset maximum number of iterations, and the preset maximum number of iterations is set to 1000 times.

7. The data-driven creep life prediction method for chromium-molybdenum-vanadium steel according to claim 1 is characterized in that: In step S6, the collected raw data is input into the trained feature extraction and feature dimension reduction model for feature processing, and the processed features are input into the classifier model for classifier training to obtain classification results; wherein the classification categories include: remaining life ≤ 25%, remaining life 25%-75% and remaining life ≥ 75%.

Images