18 results about "Discrete variable" patented technology

The invention provides a sampling method suitable for continuous-discrete mixed variable design optimization of an advanced reactor. The sampling method comprises the following steps: (1) screening an effective discrete variable combination; (2) constructing a cumulative probability distribution function of the discrete variable combination; (3) hierarchically designing a space based on a cumulative probability distribution function interval; (4) distributing the number of samples according to the interval width proportion of the cumulative probability distribution function of each layer; (5) Latin hypercube sampling is carried out on the continuous variables in each layer; (6) integrating a global sample set and verifying the quality of the global sample set; and (7) constructing an agent model and evaluating precision. According to the method, the probability characteristic of the discrete variable and the space filling characteristic of the continuous variable are integrated, efficient exploration of the nuclear reactor design space is achieved, and compared with a traditional method, the sample quality and the calculation efficiency are remarkably improved.

The embodiment of the invention discloses a data processing method, in the method, first intervention data of an intervention variable in a decision service can be utilized to perform causal effect estimation on the decision service based on an anti-fact thought, and anti-fact intervention data obtained based on the anti-fact thought can be a continuous variable. The variable can be a discrete variable, so that the causal effect estimation can be carried out on various types of intervention variables in various decision service scenes. Besides, in the method, the causal effect model can perform causal effect estimation on the input intervention variable in a group corresponding to the input confusion variable so as to obtain an output result about the result variable. Thus, the anti-fact intervention data and the first confusion data are input into the causal effect model, the first estimation result can be obtained through the causal effect model, and for a group described by the first confusion data, accurate causal effect estimation is obtained according to the anti-fact intervention data.

The invention discloses a Markov chain Monte Carlo channel estimation method based on sparse Bayesian learning, and belongs to the technical field of wireless communication. The method comprises the following steps: modeling an unknown quantity in a channel estimation problem as a random variable, and constructing a Bayesian probability graph model; structural Gaussian mixture prior distribution is introduced for channel elements, and Gaussian mixture prior is converted into a product form through Bernoulli discrete variables; and deducing conditional posteriori distribution of all variables, performing high-dimensional sampling by using a plurality of parallel Gibbs samplers, collecting convergence samples, and averaging to obtain a channel estimation value. According to the method, the sparse Bayesian learning and the Markov chain Monte Carlo method are combined, the problems that a traditional compressed sensing algorithm is insufficient in dependence on prior information and prone to falling into local optimum are solved, the channel estimation precision is remarkably improved, meanwhile, the calculation complexity is reduced through parallel sampling, and the method is suitable for a super-large-scale MIMO system.

The invention discloses a one-dimensional structured data hypothesis test evaluation method based on zero-knowledge proof, and aims to verify and evaluate the distribution consistency of synthetic data and real data under the condition of strictly protecting the real data and sensitive statistical information thereof. The method comprises the following steps: firstly, for discrete variables and continuous variables, respectively calculating an expected frequency and an observation frequency by adopting a category frequency statistics mode and an equal-width binning mode, and measuring the distribution deviation of synthetic data and real data by adopting chi-square goodness of fit test; and then reconstructing the calculation process of the test statistics into an equivalent form only containing integer addition and multiplication so as to adapt to finite field calculation. And based on the integer formula, constructing a constraint circuit and generating a zero-knowledge proof. According to the invention, a prover can prove the correctness of the hypothesis test result to a verifier under the condition that real data is not disclosed, so that the privacy of the real data is guaranteed, and the credible evaluation of the effectiveness of the synthetic data is realized.

This invention discloses a linear guide rail SOMC design method, equipment, medium, and program product, including: (1) constructing a simulation model and SOMC design model with static stiffness as the target and weight and maximum contact stress as constraints based on the structural characteristics and static load analysis of the linear guide rail; (2) constructing a design space, generating an initial population and establishing a database, and obtaining a set of high-influence continuous variables and a set of high-influence discrete variables through SRC and CEA sensitivity analysis methods; (3) generating corresponding candidate sets based on the two sets of variables, and obtaining a complete set of candidate guide rails through individual pairing; (4) establishing a random forest prediction model, screening excellent subsets and selecting the best offspring guide rails; (5) updating the database and prediction model after simulation evaluation, returning to step (3) until the indicators meet the standards, and outputting the optimal parameter values. This invention can effectively balance the SOMC process with stiffness as the optimization target and weight and maximum contact stress as constraints, achieving higher accuracy and better overall performance.

The disclosure provides a method and device for generating an adversarial sample, the method for generating an adversarial sample comprising: obtaining a training sample set and a machine learning model, wherein each training sample in the training sample set has a discrete field attribute, and the discrete field attribute refers to a field attribute with a discrete variable as a field value; converting the field value of each training sample on each discrete field attribute into a field value vector to obtain a vector sample; applying a perturbation to the field value vector of each vector sample on each discrete field attribute, and taking the vector sample after the perturbation as a first perturbed sample; and generating an adversarial sample based on the first perturbed sample. The method and device for generating an adversarial sample according to the disclosure solve the problem of difficulty in generating an adversarial sample for discrete variable data, and can generate an adversarial sample for adversarial learning for data with a discrete variable by converting the field value on a discrete field attribute into a field value vector.

The invention provides a switching power supply system-oriented efficient global optimal multi-physics co-simulation method and system, and the method comprises the steps: defining a hybrid design space containing discrete variables and continuous variables, and constructing a composite objective function; generating a small number of parameter combinations of the discrete variables and the continuous variables by adopting a mixed horizontal orthogonal array to form an initial sample set; performing multi-physical field simulation on the initial sample set to obtain a performance observation value; constructing an initial Gaussian process proxy model based on the initial sample set and the performance observation value; a discrete forced continuous random sampling strategy is adopted to generate a large number of global uniform samples, iteration is carried out through a Bayesian optimization framework based on the initial Gaussian process proxy model, and optimal candidate points are obtained; and when the composite objective function converges or reaches the maximum number of simulation times, outputting a global optimal design parameter combination.

The invention discloses an all-working-condition risk quantification method applied to a new energy civil aircraft power system, which comprehensively considers the influence of discrete factors and continuous factors, quantifies the performance risk of the power system under all working conditions into a probability density distribution function, and comprises the following steps: defining a discrete variable and continuous variable distribution set; establishing a power system performance change degree probability density function PDFcon under the influence of continuous variables; establishing a power system performance probability density function PDFsys under the common influence of the continuous variable and the discrete variable; and step 4, quantifying the system risk, namely quantifying the system risk under the common influence of the discrete variable and the continuous variable into a probability density distribution function for evaluating the risk of the system at the moment. According to the method, quantitative analysis of all-working-condition risks can be realized, and reliable support is provided for airworthiness verification, safety evaluation and engineering application of a new energy power system.

The invention provides an optimal design method and system for a material preparation formula and a medium. The optimization design method of the material preparation formula comprises the following steps: acquiring experimental data including different formula combinations and corresponding performance indexes thereof; performing formula variable space design on the basis of the category and value rule of formula variables in the experimental data and formula constraint conditions to determine a formula search boundary comprising discrete variables and continuous variables; based on a target acquisition function, performing search optimization in the formula search boundary by adopting particle swarm optimization in combination with a branch and bound method so as to determine a plurality of candidate formula combinations, the target acquisition function being defined based on performance prediction values and prediction uncertainty of the candidate formula combinations determined by a support vector regression model; and in response to a termination condition meeting search optimization, determining the candidate formula combination with the optimal target acquisition function value as a formula design scheme.

The present invention discloses a method for causal tracing of heterogeneous time series data faults, which converts discrete variables into latent continuous variables with higher information granularity through context-adaptive excitation Gaussian kernel embedding, thereby realizing causal discovery in a unified continuous space; in the potential continuity recovery stage, the guiding information of continuous variables is introduced by designing prediction tasks, and the parameters of context-adaptive excitation Gaussian kernel embedding are adjusted in a self-supervisory manner to enhance its ability to restore potential continuity, and the reversibility of the recovery process is ensured through a reconstruction mechanism; in the causal structure learning stage, key causal relationships are screened through sparsity regularization constraints, and an overall causal graph is constructed to provide support for fault analysis. The present invention can accurately determine the time series causal relationship in the heterogeneous variable scenario, provide important and reliable information for tracing the root cause of process failures, and help accurately diagnose the source of the failure, thereby effectively ensuring the safety of actual production.

The invention discloses a TBM construction core database construction method and system, and belongs to the technical field of TBM tunneling big data processing. Comprising the following steps: grouping TBM tunneling parameter data to obtain a continuous variable set and a discrete variable set; corresponding quantization modes are selected to obtain the correlation between continuous variables, the correlation between discrete variables and the correlation between the continuous variables and the discrete variables respectively, multiple sets of strong correlation variables are obtained and combined and classified to obtain a plurality of strong correlation parameter sets, and for each strong correlation parameter set, the correlation between the continuous variables and the discrete variables is obtained. And selecting one variable as a key parameter to perform dimension compression to obtain a simplified parameter set, and using the simplified parameter set as a core database for later data modeling and analysis processes. According to the method, compression of the TBM tunneling parameter data in the parameter dimension is achieved, and the calculation cost of subsequent data modeling and analysis tasks is reduced.

The invention discloses a metamaterial structure parameter optimization method based on continuous-binary discrete mixed variables. The method comprises the following steps: firstly, establishing a metamaterial structure parameterized model which comprises an input variable, a boundary condition and a target function; establishing a kernel function of a continuous-binary discrete mixed variable, wherein the kernel function is used for identifying continuous and binary discrete variables in a Gaussian process; then, an input space is initialized based on Sobol static sampling; constructing a multi-task Gaussian process agent model, and training the agent model; and finally, constructing an acquisition function, and calculating the overall hypervolume lifting amount of the smooth expectation by using the acquisition function to guide the next sampling decision. By monitoring the average hypervolume increase amount, whether optimization needs to increase the number of input vectors or whether the input vectors are converged or not is judged. By constructing an optimization framework suitable for continuous-binary discrete mixed variables, dual-performance optimization under a small sample condition is realized, the optimal structure configuration is efficiently obtained, and the optimization precision and the convergence speed are improved.

The invention discloses an axial plunger pump residual life prediction method and system based on a physically inspired multi-stage Wiener process, and belongs to the technical field of axial plunger pump residual life prediction.The axial plunger pump residual life prediction method comprises the steps that the system analyzes a wear failure mechanism of a key friction pair of an axial plunger pump, deduces a leakage rate calculation model of each friction pair, and determines the leakage rate as a degradation index; establishing a three-stage Wiener degradation model considering the uncertainty of the change point, and reducing the influence of a change point identification error on prediction precision by introducing a fusion transition stage to replace discrete change points; a parameter estimation method fusing an EM algorithm and a Newton-Raphson algorithm is provided, the problem that the multi-stage model is high in parameter dimension and prone to falling into local optimum is solved, and the model parameter estimation precision and efficiency are improved; on the basis of the Bayesian theory, online self-adaptive updating of the hyper-parameter of the drift coefficient is achieved; and deducing a residual life probability density function in combination with a first arrival time concept. The problem of uncertainty of change points can be effectively solved, and the index precision is high.

The embodiments of this application belong to the field of artificial intelligence and relate to a high-dimensional feature extraction method, comprising obtaining feature data of raw high-dimensional data, constructing an observation data sample, wherein the observation data sample includes discrete variables and continuous variables; obtaining category labels, grouping the continuous variables according to the category labels to obtain grouped variables, and calculating the sorted sum of the grouped variables; calculating the sorted sum based on a preset detection algorithm to obtain evaluation parameters, transposing all evaluation parameters to obtain a correlation vector, and performing feature screening on the raw high-dimensional data based on the correlation vector to obtain marker features; obtaining a target feature screening model and continuous parameters, inputting the marker features and continuous parameters into the target feature screening model, and calculating target dimensionality reduction features. This application also provides a high-dimensional feature extraction device, a computer device, and a storage medium. In addition, the target dimensionality reduction features can be stored in a blockchain. This application achieves accurate feature extraction from high-dimensional data.

The present application relates to the technical field of data analysis, in particular to a RIS mode selection method based on a grey correlation clustering method. It comprises the following steps: S1. A base station acquires communication data of a user, and divides the communication data of the user into two types of data: numerical data with continuous values and type data with discrete values; S2. For the type data with discrete values, a grey possibility degree mapping matrix is established to directly map the discrete variables to the final mode classification; S3. For the numerical data with continuous values, a grey possibility degree function is established to first map the continuous variables to connotation classification, and then to the final mode classification; S4. A weight is set to statistically summarize the prediction scores of each mode; and S5. The optimal decision for the RIS mode is selected according to the ranking of the prediction scores. This method can accurately capture user data while reducing the complexity of calculation, and is more suitable for real-time application under a large model.

The invention relates to the field of large power grid reliability evaluation, in particular to a power grid reliability evaluation cross entropy method based on normal space vector optimization. Comprising the following steps: step 1, transforming a system random variable from a mixed variable space of an original discrete variable and a continuous variable to a multivariate standard normal space through a normal transformation method to obtain a standard normal random vector u; u and coordinates are expressed, an IS-PDF of a direction is established based on a von Mises-Fisher model, an IS-PDF of a radius is established based on Nakagami distribution, and the two phases are combined to form a multivariate normal space normal random vector distribution model; and iterative optimization is carried out by using a cross entropy method based on normal random vector importance sampling to obtain an optimal parameter of the sampling PDF, so that the absolute optimal PDF of the system is approached. According to the method provided by the invention, especially in a power system with a more complex topological structure and higher dimensionality, an optimization result is easier to highlight the density of multi-peak and irregular fault domains, and an evaluation result is obviously superior to that of a traditional CE method.

The application relates to a data binning method, device, computer equipment and storage medium. The method comprises the following steps: acquiring business data; dividing the business data into discrete variables and continuous variables; automatically binning each continuous variable to obtain a plurality of variable binning results corresponding to the continuous variable; in response to the number of categories of the discrete variables being less than or equal to a minimum category number threshold, outputting the discrete variables; in response to there being at least one discrete variable with a category number greater than the minimum category number threshold, automatically binning each discrete variable to obtain a plurality of variable binning results corresponding to the discrete variable; according to a preset evaluation index, screening the plurality of variable binning results corresponding to each continuous variable and / or discrete variable to obtain an optimal variable binning result; and outputting the optimal variable binning result and a parameter range of a corresponding binning method. The method can reduce the system calculation pressure, and can comprehensively obtain a plurality of variable binning results of data and an optimal variable binning result.

The application discloses a TBM construction core database construction method and system, and belongs to the tunneling big data processing technical field of TBM. The method comprises the following steps: grouping TBM tunneling parameter data to obtain a continuous variable set and a discrete variable set; selecting corresponding quantization modes to obtain the correlation between continuous variables, the correlation between discrete variables and the correlation between continuous variables and discrete variables, and obtaining a plurality of groups of strongly correlated variable pairs, and classifying and merging to obtain a plurality of groups of strongly correlated parameter groups; for each group of strongly correlated parameter groups, selecting one variable as a key parameter to perform dimension compression to obtain a simplified parameter set; and taking the simplified parameter set as a core database for subsequent data modeling and analysis processes. The application realizes the compression of TBM tunneling parameter data in the parameter dimension, and reduces the calculation cost of subsequent data modeling and analysis tasks.