206 results about "Latent variable" patented technology

The invention relates to a temperature field prediction and parameter inversion method based on a physical constraint generative adversarial network. The method comprises the following steps: acquiring sensor data and sampling random latent variables; constructing a generative adversarial model comprising a generator, a discriminator, an auxiliary parameter generator and an auxiliary variational encoder based on sensor data and a physical constraint partial differential equation; by minimizing reverse KL divergence and introducing physical consistency constraints, boundary condition constraints and information entropy regularization items, parameters of a generative adversarial model are jointly optimized; after offline training is completed, new space-time coordinates and random latent variables are input, a temperature field prediction result is obtained through a trained generator, and PDE parameter estimation of a corresponding position is obtained through an auxiliary parameter generator. Compared with the prior art, the method can perform unified modeling and prediction on the space-time dynamic system dominated by the random partial differential equation, and has generalization ability under uncertainty quantization, physical parameter estimation and sparse observation data.

The invention discloses an electricity meter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling and a storage medium, and relates to the technical field of electric energy metering and detection, and the method comprises the steps: constructing a multi-physical coupling basis vector containing a single variable and a cross variable based on a sensing output signal; hidden variable joint modeling is carried out based on the multi-physical coupling basis vector and parasitic capacitance change, magnetic conductivity drift and shunt resistance offset, and a compensation mapping relation of electromagnetic, thermal and stress coupling is formed; generating a compensation coefficient by adopting the compensation mapping relation, and applying the compensation coefficient in an ammeter metering link to complete an error compensation process; according to the method, voltage, current and phase compensation coefficients are generated through multi-physical coupling basis vector and hidden variable modeling, dynamic description and self-adaptive correction of error sources are achieved, dynamic accurate correction of voltage, current and phase is achieved, and it is ensured that the ammeter keeps high-precision metering for a long time under the complex environment and the device drift condition.

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

The invention relates to the technical field of artificial intelligence, in particular to a diffusion model preference optimization method and system based on adaptive gradient adjustment, and the method comprises the steps: carrying out the de-noising processing of an initial data sequence, generating a generation sample similar to real distribution, and carrying out the calculation of the preference of a diffusion model according to the sample generated by the initial data sequence in combination with human feedback information; a diffusion model is directly optimized through an adaptive gradient adjustment mechanism, a human preference data sample is constructed, the preference of a user to generated content is reflected, the gradient update weight of the denoising process is dynamically adjusted through a gradient weight function, the function is adaptively adjusted according to the time step and the hidden variable frequency characteristic, and by adjusting the gradient update of the denoising process, the user experience is improved. According to the method, the initial data sequence parameters are optimized, the optimized data sequence is obtained, the optimal hidden variable is calculated according to the optimized data sequence, the optimal hidden variable is adopted to generate the data sample conforming to the human preference, an explicit reward function does not need to be constructed, optimization is achieved only through indirect modeling of the human preference sample, and the model optimization process is simplified.

The invention provides a privacy protection multi-energy load day-ahead probability prediction method based on a diffusion model, and belongs to the field of multi-energy load prediction of an integrated energy system. Comprising the following steps: S1, acquiring respective energy consumption load data by a local main body, and preprocessing and normalizing the data; s2, dividing the energy consumption data into historical loads and labels, and respectively training two groups of self-encoders-self-decoders; s3, the local main body uploads the latent variables obtained after coding to the cloud; s4, grouping and integrating latent variables by the diffusion model of the cloud, and constructing a joint probability prediction model; and S5, during model reasoning, after a latent variable generated by the cloud is downloaded locally, a final predicted value is obtained through decoding by a self-decoder. According to the method, a prediction framework based on longitudinal federated learning is designed for a multi-energy load probability prediction scene related to a cross-energy form in an integrated energy system, and a diffusion model capable of predicting multi-energy load joint probability distribution is constructed by considering a coupling relationship among multi-energy loads.

The invention belongs to the field of intelligent transportation, and relates to a future feature enhanced vehicle trajectory prediction generative adversarial method, which comprises the following steps: a conditional information learning step: completing joint modeling of historical and future features; a generative adversarial training step: performing multi-modal trajectory prediction by adopting a CVAE-GAN hybrid architecture; wherein the CVAE-GAN hybrid architecture comprises an encoder, a generator and a discriminator; the encoder comprises a regression encoder and a prior encoder, and the regression encoder is responsible for mapping a real track to a submerged space, learning compact representation of the track and only being used during model training; the priori encoder performs network sampling to obtain a latent variable; the generator reconstructs a multi-modal future trajectory according to latent variables and condition information; the discriminator receives the trajectory and the condition information, and evaluates the authenticity of the trajectory and the consistency of the trajectory and the condition information. By designing a double-stage strategy, stable generation and optimization of multi-modal trajectory distribution are realized, and the problems that traditional GAN training is unstable and CVAE output is too smooth are solved.

The invention discloses a distillate oil property prediction method based on deep learning feature extraction and partial least squares regression. The method comprises the following steps: firstly, carrying out classification training on a near infrared spectrum through a convolution-attention double-branch fusion network, and extracting high-dimensional spectral features with local and global information; then, historical samples are retrieved from a database based on prediction categories, a plurality of most similar samples are selected by adopting cosine similarity measurement to construct a correction set, and the spectral features and property labels are subjected to standardization processing; and finally, carrying out partial least squares regression modeling on the correction set, extracting latent variables to maximize covariance between spectral features and physicochemical properties, and inputting feature vectors of an oil sample to be detected into the trained PLS model to obtain a corresponding property prediction result. According to the method, the modeling requirement and the category specificity characteristics under the small sample condition are considered while the prediction precision is guaranteed, and the method is suitable for rapid property detection and intelligent analysis in the refining process.

The invention provides a virtual sample generation method and system based on a non-stationary neural network Gaussian process, and the method comprises the steps: obtaining original data, judging the non-stationarity, and judging whether a statistical characteristic changes with an input position or not; if not, a hidden variable model fusing the neural network and the Gaussian process is constructed, and a hidden variable space representing non-stationary distribution is obtained through training; sampling based on hidden variable space probability distribution; using the model to generate = (, Z) (Z, Z)-1X through non-stationary high-dimensional mapping, which is a non-stationary kernel function, Z is a low-dimensional hidden variable, and X is an input variable; checking the consistency of the high-dimensional virtual samples and the original data, and screening a virtual sample set meeting statistical consistency; according to the method, high-quality and high-diversity virtual samples can be generated in small sample and non-stationary scenes.

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

The invention discloses a quality evaluation method and system for a composite board, and relates to the technical field of quality detection, and the method comprises the steps: constructing a latent variable feature space representing interface binding energy abnormity based on an initial response data field through a physical constraint relation between interface local response features and binding energy distribution; extracting a topological correlation structure between local latent variable features in the latent variable feature space through a graph convolutional network to obtain a local binding energy anomaly initial candidate region; active response mode enhanced excitation is carried out on the local area of the initial candidate area, the dynamic enhanced response of each local area is obtained, and a binding energy abnormal area is determined; determining the specific position and range of the hidden pseudo solder strip according to the correlation topological structure difference between the local feature in the binding energy abnormal region and the normal region; according to the method, precise identification and high-precision positioning of the titanium-steel composite plate interface hidden cold solder strip are realized.

The invention discloses an aircraft aerodynamic configuration optimization design method based on manifold learning dimensionality reduction mapping, and relates to the technical field of aerodynamic configuration design, and the method comprises the steps: defining design variable parameters of an aircraft aerodynamic configuration, and determining a design variable parameter sample and pressure distribution sample data according to a variable value range; training a preset Bayesian manifold learning dimensionality reduction mapping model, and determining a latent variable by using the trained target Bayesian manifold learning dimensionality reduction mapping model and the pressure distribution sample data; training a preset multi-output deep neural network model based on the design variable parameter sample and the latent variable; and performing pressure distribution prediction based on the trained target multi-output deep neural network model, the target Bayesian manifold learning dimensionality reduction mapping model and the variable value range, optimizing target pressure distribution in the variable value range by using a pressure distribution prediction result, and determining a target design variable parameter value. The problem of high-dimensional mapping precision in existing related schemes can be solved, and the design accuracy is improved.

The invention discloses a material performance prediction method and system based on deep learning, and relates to the technical field of material performance prediction, and the method comprises the steps: building a cross-level message passing network based on a double-layer graph data structure, extracting atomic-level and functional group-level feature embedding, and generating a molecular global latent variable through bidirectional cross-scale attention interaction; fusing molecular global latent variables with atomic-scale and functional group-scale features, applying physical constraints in combination with a chemical bonding rule base and a group contribution theory, and constructing a physical property prediction model; obtaining candidate molecules through autoregression generation and molecular force field verification based on a molecular global latent variable and a physical property prediction model; and screening candidate molecules by using the physical property prediction model, and outputting a final molecule set through molecular dynamics simulation and synthesis feasibility evaluation verification. Through multi-stage verification of molecular dynamics simulation and synthesis feasibility evaluation, a high-tension ring or a non-synthesized structure is effectively eliminated, so that the generated molecule has high performance and manufacturability.

A method for generating a principal predictor model from multi-dimensional time series data, comprises: receiving, from a plurality of sensors, multi-dimensional time series data corresponding to a plurality of original variables; transforming the multi-dimensional time series data to a lower dimension to define reduced-dimensional time series data; extracting, by a controller, a plurality of latent variables from the reduced-dimensional time series data and determining values of the plurality of latent variables in a first time period; initializing, by the controller, a loadings matrix corresponding to a set of latent variables of the plurality of latent variables; and determining, by the controller, one or more principal predictor model parameters, by performing an iterative process. The iterative process comprises (a) predicting values of the plurality of latent variables based on the reduced-dimensional time series data and the loadings matrix, by using an estimation process which maximizes a covariance between the values of the plurality of latent variables and the predicted values of the plurality of latent variables; (b) calculating a new loadings matrix from the loadings matrix and the predicted values of the latent variables; (c) updating the loadings matrix based on the calculated new loadings matrix; and (d) iteratively repeating (a) to (c) until the one or more principal predictor model parameters reach convergence.

The invention relates to the technical field of speech synthesis, and particularly discloses a speech synthesis method and system for controllable latent variable modeling based on semantic distillation, and the method comprises the steps: converting a Mel spectrum into continuous latent variable distribution through a speech coding module, generating continuous latent variables through re-parameterization sampling, introducing a self-supervised model for semantic distillation, and carrying out the semantic distillation. According to the method, alignment of latent variables and semantic features is constrained through marginal cosine similarity and distance matrix structure loss, a text encoder maps a phoneme sequence into latent variable distribution, time sequence alignment of a text and the latent variables is achieved in combination with monotonic alignment search, and a decoder reconstructs the latent variables into a Mel spectrum. According to the method, waveform synthesis through a vocoder and total loss function joint optimization reconstruction, KL divergence, distillation, text alignment and confrontation loss are carried out, discrete information loss is avoided through continuous latent variable modeling, semantic consistency and text alignment efficiency are enhanced, the naturalness, coherence and real-time performance of synthesized voice are improved, and the method is suitable for scenes such as voice assistants and virtual anchors.

This invention discloses a stepwise data assimilation method for a set subspace in a nonlinear inverse problem, relating to the field of data processing technology. The invention constructs an initial prior physical set based on the physical state variables to be inverted and optimized, and historical observation data. It extracts the static subspace basis anomaly matrix, initializes the latent variable set and particle weights, calculates fractional-step incremental reweighting, evaluates the current likelihood mismatch penalty using predicted data, updates and normalizes the particle weights in the logarithmic domain, obtains the effective sample number, performs system resampling operations in conjunction with a preset resampling tolerance coefficient, eliminates low-weight particles and replicates high-weight particles, synchronously updates the latent variable set and predicted data, executes a dynamic MCMC mutation loop to generate proposed latent state vectors and affinely maps them to a high-dimensional physical space, and updates the latent variable set by evaluating the annealing target energy within the latent variable subspace until all fractional steps are traversed. Finally, it outputs the latent variable set and maps it back to the physical space.

The invention belongs to the technical field of intelligent shield construction, and particularly discloses a shield tunneling machine posture multi-step prediction and deviation correction control method based on latent variable enhanced Free Transform. Firstly, shield construction process data are obtained, and a control quantity set is established and preprocessed; error features, change rate features and unit mileage features are constructed based on the preprocessed data, and a feature sequence is formed after normalization; organizing the sequence into a token sequence with a single time step, and inputting the token sequence into a pre-trained Free Transform model after position and mileage coding; the model aggregates complete sequence information through a non-causal encoder dedicated block, calculates latent variable distribution parameters based on a hidden state sequence, samples to obtain latent variables, maps the latent variables into conditional vectors and broadcasts the conditional vectors to a decoding path; and finally, outputting a future shield attitude vector, an over-threshold probability and a recommended control quantity in parallel by a multi-task output head. According to the method, working condition self-adaptive multi-step prediction and deviation correction are realized, and the safety and intelligent level of shield construction can be improved.

The invention discloses a roadway driving face roof fall risk non-contact multi-modal fusion real-time sensing and early warning method, which comprises the following steps: acquiring multi-modal heterogeneous data in a non-contact monitoring mode, the multi-modal heterogeneous data being data of different formats from different data sources; performing feature extraction on the multi-modal heterogeneous data to obtain multi-modal features, performing key feature screening and dimensionality reduction optimization on the multi-modal features, and calculating roof fall risk latent variables according to the key features after dimensionality reduction optimization; and extracting time sequence change characteristics of the roof fall risk latent variable, and making a decision according to the time sequence change characteristics to obtain a roof risk stability grade and a support strategy.

The invention provides a time series data anomaly detection method, device and equipment, and belongs to the field of data processing. The method comprises the following steps: mapping target time sequence data to a potential space through a preset encoder to obtain an initial potential variable; performing fuzzy reasoning on the initial potential variable to obtain a target potential variable and a membership degree of the initial potential variable in a target category, wherein the target category indicates a normal data category or an abnormal data category; obtaining generation data corresponding to the target potential variable according to the target potential variable and a preset generator; obtaining a judgment score according to the generated data and a preset discriminator, wherein the preset discriminator is used for judging whether the generated data is real data or not; and obtaining a target score according to the target time sequence data, the generation data, the membership degree and the judgment score, wherein the target score is used for indicating whether the target time sequence data is abnormal or not. The accuracy of abnormal data judgment and the generalization ability of the model are improved.

The invention discloses a bridge bearing capacity dynamic evaluation method based on multi-source monitoring data driving, and aims to solve the problem that the bearing evaluation precision is difficult to guarantee under a sparse sensing coverage condition. According to the method, through cross-modal attention alignment guided by event anchor points, mask codes influencing line and propagation time delay constraints and geometric position coding, Green function kernels and time-varying boundary flexibility latent variables are introduced into a physical constraint neural operator model, symplectic integral propulsion is adopted, and block shape-preserving interval estimation and order-preserving calibration weighted according to working conditions are adopted. According to the method, the full-bridge space-time response and the bearing utilization coefficient are calculated, the risk level and the load limiting suggestion are output, and the technical effects of reliability improvement, physical consistency and long-time energy stability evaluation under the complex working conditions and the low signal-to-noise ratio are achieved.

The invention discloses a hydroelectric generating set fault diagnosis data enhancement method based on a diffusion model and generative adversarial training, and belongs to the field of industrial equipment fault diagnosis. According to the method, the data enhancement model combining the diffusion model and the generative adversarial network is constructed, a multi-modal non-Gaussian distribution mechanism and a latent variable control generation process are introduced, the limitation of a single Gaussian hypothesis of a traditional diffusion model is broken through, and richer and more real sample generation is realized. And a condition discriminator and a time sequence modeling mechanism are introduced, so that the training of the model under different noise levels is more stable, and the authenticity judgment capability of the discriminator on the generated sample is enhanced. The problems that an existing hydroelectric generating set fault diagnosis system is insufficient in sample, low in generated sample quality and unreal in sample distribution are solved, and the diversity and quality of data are remarkably improved.

A method performed by a computing device for a radio network for configuration of a network device on which network or data energy can be collected while preserving specified conditions in the radio network is provided. The method includes receiving inputs to a conditional generative model. The inputs include the specified conditions in the radio network including a value for a predicted key performance indicator, KPI, constraint parameter for a time period, a target value for a optimization parameter, and a latent variable. The method further includes outputting from the conditional generative model a configuration data for a future time period for the network node or the cell of the radio network. The configuration data is bounded by the specified conditions including the predicted KPI constraint parameter, the target value for the optimization parameter, and the latent variable.

The invention discloses a robot action generation method integrating multi-layer feature bridging and world knowledge prediction. The method comprises the following steps: extracting multi-layer middle layer visual features and action query hidden variables by utilizing a pre-trained visual language model; an initialization strategy based on robot body sensing state guidance is adopted, real-time pose priori is injected for action query, and traditional all-zero initialization is replaced; a spatial perception vector gating mechanism is introduced into the bridging attention module, and fine-grained selection of specific image region features is achieved; meanwhile, a world knowledge prediction task is integrated, and physical common knowledge is enhanced through explicit modeling environment depth, semantics and a dynamic region; and finally, replacing a traditional L1 regression action head with a diffusion model architecture, and generating an optimal action sequence under multi-modal distribution through an iterative denoising process. According to the method, the operation precision of the robot in a long-range complex task is improved, and the problem of control failure caused by an action'averaging effect 'is solved while the light weight of the model is kept.

The invention relates to the technical field of multi-objective optimization algorithm verification and intelligent decision making, in particular to an algorithm verification method and application system based on multi-objective optimization, and the method comprises the steps: obtaining data, and forming a training data set; performing joint representation learning and decoupling on a Pareto solution set based on the data set to obtain independent latent variables of three dimensions of business stability, performance expression and environmental sensitivity; constructing simulation environment associated environment sensitivity information and disturbance parameters, generating a training scene sequence to drive an intelligent body to explore, and outputting a robustness evaluation result; learning an implicit preference function in combination with an implicit interaction sequence of a decision maker, recommending a candidate solution set and adjusting network parameters according to feedback; and establishing a knowledge graph to store historical data, and performing joint optimization on system parameters by extracting experience. According to the method, interpretability representation of the Pareto solution set, robustness verification in a dynamic environment and adaptive learning of decision preference are realized, and algorithm verification efficiency and decision reliability are effectively improved.

The invention discloses a house safety level prediction method and system based on a Bayesian network, and relates to the technical field of house safety evaluation. The method comprises the following steps: acquiring structural characteristic parameters, environmental influence factors and historical state information of a target house; performing three-level quantization processing on the structural characteristic parameters to obtain a structural evaluation matrix, and constructing a dynamic attenuation function based on the environmental impact factors to calculate a dynamic attenuation weight; generating a historical state index based on the historical state information through a multi-dimensional feature fusion technology; based on the structure evaluation matrix, the dynamic attenuation weight and the historical state index, calculating the structure redundancy as a hidden variable; constructing a three-layer Bayesian network; and probability propagation calculation is carried out through a Bayesian network inference engine, and the house safety level of the target house is determined according to the probability distribution of the output layer. By implementing the technical scheme provided by the invention, the actual safety level of the house can be accurately evaluated and predicted.

Apparatuses and methods are disclosed for encoding and decoding data. Techniques disclosed provide for the encoding of a data region of a frame. The encoding includes training an INR network to produce the data region from latent variables. And, determining distributions of the latent variables using respective contexts, constructed based on latent variables located within a shaped context region. Then, coding into a bitstream the latent variables based on the determined distributions and further coding into the bitstream the parameters of the trained INR network. Techniques disclosed also provide for decoding the data region. The decoding includes decoding from the bitstream the parameters of the INR network, determining the distributions of the latent variables using the respective contexts, and decoding from the bitstream the latent variables based on the determined distributions. Using the parameters of the INR network, the INR network produces the data region from the latent variables.

The present invention relates to a method for dynamically selecting a photovoltaic power generation prediction model. More specifically, the method for dynamically selecting a photovoltaic power generation prediction model may comprise the steps of: training a dynamic prediction model selector for photovoltaic power generation prediction; and selecting, via the trained dynamic prediction model selector, an optimal prediction model for minimizing a photovoltaic power generation prediction error with respect to weather data and solar information data. The training step includes: (a) a step of learning, with an auto-encoder, weather data and solar information data for a plurality of photovoltaic power plants to extract a plurality of latent variables; (b) a step of classifying the plurality of extracted latent variables into a plurality of clusters on the basis of similarity; (c) a step of training, with the cluster-specific latent variables, a plurality of different prediction models; and (d) a step of dynamically selecting an optimal prediction model for each of the clusters from among the plurality of trained prediction models.

The invention discloses a latent variable guided unsafe behavior intervention system and a latent variable guided unsafe behavior intervention method for building workers. A decision laboratory-interpretation structure model is used for analyzing the mutual relation between unsafe behavior factors such as unsafe psychology, unsafe physiology, safety ability and unsafe motivation of the building workers and safety culture, safety atmosphere and safety education. And determining an action path of the intervention measures on the influence factors. And constructing an unsafe behavior occurrence mechanism and intervention mechanism model in combination with the structural equation model. And then simulation research and analysis of the action effect of the intervention strategy are carried out by using system dynamics, and the internal occurrence mechanism and intervention effect of the unsafe behaviors of the building workers are deeply analyzed. The method has the advantages that the relation between unsafe behavior occurrence and intervention paths of workers is studied statically, the unsafe behavior occurrence and intervention process is studied dynamically, the safety management level in the building construction process is improved, and safety accidents are reduced.