128 results about "Probit model" patented technology

The invention belongs to the technical field of turbulent flow field prediction and deep learning, and discloses an incompressible turbulent flow field prediction method based on a potential diffusion model. The method comprises the following steps: acquiring original turbulence data; processing the turbulence data; constructing a turbulence prediction model; model training; and evaluating the model and the like. The model of the technical scheme of the invention specifically comprises the following steps: designing a multi-scale Fourier auto-encoder for extracting multi-scale space and frequency domain features in a turbulence field and obtaining a global structure and a local scale structure of turbulence; a novel accelerated sampling method is proposed and introduced in the diffusion process, namely a diffusion probability model solver greatly shortens the reasoning time in a potential space and keeps high fidelity in long-time-sequence prediction; a physical constraint loss item based on a partial differential equation is introduced, and a Navier-Stokes equation (N-S) is explicitly introduced into a training process, so that the physical consistency of results is effectively improved, and errors are remarkably reduced.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

Techniques for concept shift detection and correction using probabilistic models and learned feature representations are described. A gaussian process model is trained using representations generated by a primary machine learning (ML) model for existing training data elements in a training memory. For a new batch of data elements, representations again generated by the primary ML model can be used as input for the gaussian process model to generate predictive distributions. When the true targets for the new data elements are not sufficiently likely according to the corresponding predictive distributions, concept shift is likely and the training memory can be purged of the existing data elements before further retraining of the primary ML model.

The invention discloses a multi-group time sequence student cognitive diagnosis method based on a joint probability model, which belongs to the field of education and comprises the following steps: acquiring student answer data, group classification labels and covariable data of at least two continuous time points; according to the student answer data, the group classification label and the covariable data, a joint probability model is constructed, and the joint probability model comprises a structure model and a measurement model; the structure model is used for describing a dynamic change process of a potential knowledge mastering state of the student, and the measurement model is used for establishing an association relationship between answer data of the student at each time point and the potential knowledge mastering state; and performing analysis based on the joint probability model to obtain the potential knowledge mastering state of the student. According to the method, the potential knowledge mastering state and response mode difference can be compared among multiple groups, and an important basis is provided for evaluating educational fairness and optimizing teaching strategies.

The invention discloses a high-dimensional time series data classification method and device based on multi-scale diffusion denoising. According to the method, a multi-scale condition guidance strategy is introduced to guide the denoising process of time sequence labels, and condition priori of different scales is used in the diffusion process to adjust each step. The method comprises the following steps of: encoding time sequence data from two angles of variable and time, extracting features, obtaining multi-scale feature representation of a time sequence, mining a dependency relationship of the sequence on a time dimension and a variable dimension by using a double attention mechanism, and generating condition priori on different scales; a standard denoising diffusion probability model is used for training a process, a fusion vector in a potential space is obtained by using a multi-scale encoder, then the fusion vector and a time step are embedded, and a full connection layer is used for predicting noise. According to the method, the result accuracy of the high-dimensional multivariable time series in the classification task and the robustness of processing irregular time series data containing missing values are improved.

The invention relates to a DEMATEL-BWM fusion assembly sequence evaluation method based on a Bayesian probability model. Assembly sequence evaluation is carried out on a complex mechanical object. Collecting data, establishing a direct influence matrix, normalizing the direct influence matrix into a standard influence matrix, performing Bayesian updating, and performing exponential transformation processing on an output posteriori mean value to obtain a preference matrix; determining optimal and worst elements, performing pairwise comparison to obtain optimal and worst vectors, modeling the optimal and worst vectors as polynomial distribution, and modeling the output weight vectors as Dirichlet distribution; processing input of multiple decision groups through a Bayesian hierarchical model, establishing a joint probability model, and performing decomposition through conditional independence and probability chain rules; the prior distribution and observation data are used, posterior distribution is obtained through Bayesian updating, and a confidence level is used to generate a weighted directed graph; and outputting the aggregation weight, and evaluating the assembly sequence. The problem that an assembly sequence evaluation method is low in efficiency is solved, the inconsistency of subjective judgment is reduced, and the method is more fault-tolerant, concise and efficient for deviation.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention discloses an urban activity prediction method and system based on a Transform framework and a denoising diffusion probability model, and the method can achieve the prediction of the urban activity of any building in any future time under the condition that the urban activity of any historical time is known through the trained model based on the Transform framework and the denoising diffusion probability model. The construction of the model specifically comprises the following steps: carrying out fine-grained division on spatio-temporal data according to a building and room units in the building, executing Z-Score standardization, inputting a spatio-temporal causal convolution module, adjusting a data structure to adapt to a Transform structure, carrying out mask processing, and enhancing the time sequence prediction capability by adopting a noise diffusion probability model framework. According to the method, high prediction precision can be realized under the condition of relatively short input time, high-precision prediction of urban activities can be realized on the aspects of large scale and small fine granularity, arbitrary multi-step prediction of urban activities under any time step is realized by using artificial intelligence for the first time, and the blank of artificial intelligence in urban activity prediction is filled.

The invention discloses an electric power information network security risk assessment method and system, and relates to the technical field, and the system comprises the following components: S100, data acquisition, S200, artificial intelligence algorithm analysis, S300, fusion model, S400, risk assessment and S500, prediction model. According to the method, future security threats are predicted through the Markov chain Monte Carlo method and the fusion neural network algorithm, the security state of the power information network is comprehensively defined as the state space of the MCMC, and the transition probability model between the states is constructed, so that the conversion condition and probability between different states can be finely analyzed, and the security threats of the power information network can be predicted. And dynamic and accurate prediction of future security threats is realized.

The invention relates to a lithofacies intelligent pattern classification method based on a conditional diffusion probability model, and belongs to the technical field of deep learning, pattern recognition and big data processing. Comprising the following steps: step (1), collecting and preprocessing big data; step (2), constructing a conditional diffusion probability model; step (3), model training and parameter optimization; step (4), generating a category balance sample; (5) enhancing data quality evaluation; and (6) training and verifying the pattern classification model. According to the method, shale lithofacies data enhancement and intelligent mode classification based on the conditional diffusion probability model and the deep learning technology are realized, the lithofacies identification problem under the condition of training set category imbalance is effectively solved, and the minority class lithofacies identification precision and the overall classification performance of the model are remarkably improved; and a reliable technical method is provided for marine shale oil and gas reservoir evaluation and sweet spot prediction.

The invention discloses a probability model quantification method for flexibility potential of a building air conditioner, which comprises the following steps of: extracting an upper and lower bound cumulative distribution function by adopting a probability box method, and describing a flexibility fluctuation range of an air conditioning system in an interval probability form; a Wasserstein distance index is introduced, the difference of load probability distribution before and after regulation is quantified, and a flexible potential expected value is represented according to the difference. According to the method, the adjustable load range of the building can be accurately described under the multi-source disturbance condition, and reliable support is provided for flexible resource scheduling and response strategy making.

The invention provides a coal-fired unit digital twin modeling method and device based on a denoising diffusion probability model and a storage medium, and belongs to the field of digital twin modeling. The problems that an existing method is large in modeling difficulty and high in model complexity, and mechanism simplification and data quality dependence cannot be avoided are solved. Comprising the following steps: data preprocessing: selecting equipment parameters highly related to operation of a coal-fired unit as diffusion characteristics, selecting diffusion indexes according to the diffusion characteristics, and carrying out normalization processing on the diffusion characteristics and the diffusion indexes; on the basis of the probability denoising diffusion model, a lightweight MobileNet is adopted to replace a residual block in a UNet network, and a lighter and faster industrial digital twinning denoising diffusion probability model DT-DDPM is obtained; guiding a sampling process by adopting diffusion characteristics; carrying out high-fidelity digital twinborn modeling on the coal-fired unit by adopting an industrial digital twinborn denoising diffusion probability model and historical data of operation of the coal-fired unit; the method is applied to coal-fired unit digital twin modeling.

The invention discloses a multi-layer circuit board quality inspection method and system based on machine learning, and the method comprises the steps: carrying out the time-space registration of collected multi-source data through an adaptive weighted fusion algorithm, and generating a multi-mode quality inspection data set containing a line topological structure and material characteristics; outputting a fused circuit board defect sensitive feature vector set by using a pre-trained nested attention deep learning model based on the multi-modal quality inspection data set; inputting the defect sensitive feature vector set into a twin network architecture, positioning a potential defect area through a dynamic anchor frame generation mechanism, carrying out multi-label classification on defect types in combination with a Bayesian probability model, and synchronously introducing a defect severity evaluation module to quantify the influence degree of defects on circuit performance, and outputting a detection result containing the defect position type and severity. According to the embodiment of the invention, the collaborative judgment of the type, position and severity of the defect can be realized, and the detection precision and generalization capability of the defect of the multilayer circuit board are improved.

The invention provides a soil moisture sensor data quality inspection and interpolation method, which comprises the following steps: screening multivariable soil moisture time sequence data based on a preset core physical feature list, carrying out abnormal value detection through physical rule constraint and an isolation forest algorithm, and carrying out data labeling by creating a complete time axis; generating a training sample from the preprocessed data through sliding window sampling, and performing deep feature learning by using a denoising network based on a space-time diffusion probability model; performing interpolation on missing values in the original data by using the trained space-time diffusion probability model, generating a noisy data sample through a forward noise adding process, and performing conditional data interpolation based on a known observation value and a mask matrix in a reverse denoising process to generate a preliminary interpolation result; and performing post-processing correction on the preliminary interpolation result, wherein the post-processing correction comprises clamping correction based on a monthly historical range and correction based on interlayer physical logic.

The present invention discloses a carbon dioxide concentration prediction method based on a semi-supervised deep probability model for on-line detection of the carbon dioxide content in a carbon dioxide absorption tower. Based on the conventional probabilistic principal component regression model, the present invention simultaneously introduces the ideas of semi-supervised learning and deep learning, expands the basic probabilistic principal component regression model into the structure of a semi-supervised deep learning model, that is, proposes a new soft sensing method based on a semi-supervised deep probabilistic principal component regression model for on-line detection of the carbon dioxide concentration in a carbon dioxide absorption tower. Compared with the conventional principal component regression model, the method of the present invention can not only effectively utilize a large amount of cheap unlabeled sample information, but also deeply extract the information hidden in the process data, thereby improving the actual prediction effect of the carbon dioxide concentration.

The purpose is to appropriately manage abnormal communications with a simpler configuration. [Solution] The abnormality management device 1 includes a second learning unit 12 that updates only the classifier parameters of a classifier 122 that distinguishes between true normal data and pseudo-normal data, based on the probability parameters of a probabilistic model representing the number of normal received packets estimated by the first learning unit 11, in a direction that maximizes the classification accuracy, while keeping fixed the generator parameters of a generator 121 that generates pseudo-normal data that deviates sufficiently from the distribution of true normal data, taking each observed value of the series of number of normal received packets as true normal data, and an abnormal received packet number database 13 that stores the pseudo-normal data output by the generator 121 after the second learning unit 12 updates the classifier parameters of the classifier 122 as information indicating the number of abnormal received packets that deviates from the range considered to be the number of normal received packets.

The present invention discloses an intelligent system evaluation method based on the credibility of multi-model cross-identification data. The method first collects training sets and test sets through field and simulation tests. Then, a sub-model is constructed and randomly sampled from the training set to generate S sub-training sets, respectively trained S The proposed method uses a sub-model to predict the training set, generating and ranking multiple errors. A clustering algorithm is then used to identify initial untrustworthy data. The initial untrustworthy datasets for all sub-models are then counted and frequency calculated. The final untrustworthy dataset is then determined based on the frequency and removed from the training set. The remaining data after removal is considered trustworthy data. Finally, a tabular denoising diffusion probability model is used to enhance the trustworthy data. The enhanced dataset is used to train new sub-models, which are then validated using a test set. This method can accurately identify data credibility and demonstrates high compatibility across different machine learning methods.

To perform communication control about use of a radio resource to an IoT terminal so as to be an intended communication traffic state.SOLUTION: A communication control device 1 includes a first setting part 10 for setting a distribution of observation data being a set of observation values about a mixed probability model with a use state of each resource block obtained by dividing a radio resource as the observation value of each cluster, a second setting part 11 for setting a parameter of the mixed probability model so as to be a set distribution of observation data, a learning part 12 for performing adversarial learning of a generation model having a generator 121 for generating pseudo use information similar to true use information with the mixed probability model having the set parameter as the true use information about the use state of the resource block and a discriminator 122, and a notification part 14 for notifying a communication terminal 2 of a learned generator 121' constructed by the learning part 12 as communication control information.SELECTED DRAWING: Figure 1

The invention discloses an electric power system cascading failure risk assessment method, system and device based on machine learning and a medium. The method comprises the steps that an electric power system cascading failure probability model is constructed, and an accident chain of an initial failure conduction path is generated; a multi-scene initial fault data set is dynamically generated, and cascading fault risk labels are marked; classifying the multi-scene initial fault data set, storing a risk sub-data set, and outputting a corresponding fault probability; predicting a loss consequence of cascading failures of the risk sub-data set through a machine learning regression model, and quantifying a risk level; and fusing the fault probability and the loss consequence to generate a comprehensive risk assessment index. Through collaborative innovation of accurate modeling, efficient calculation, dynamic optimization and real-time control, triple breakthrough of accuracy, timeliness and operability of cascading failure risk assessment of the high-proportion new energy power system is realized, and a full-chain technical support from risk early warning to active blocking is provided for safe and stable operation of a power grid.

The invention discloses a finite sample spectral data enhancement and physiological and biochemical component inversion method based on a de-noising diffusion probability model, which comprises the following steps: collecting leaf spectral data of a plurality of plant samples, measuring leaf nitrogen content, and constructing a leaf spectrum-nitrogen content data set; the spectrum-nitrogen content data of the leaves are input into a denoising diffusion probability model for data enhancement, original data distribution is degraded into analyzable distribution by gradually adding Gaussian noise in a forward diffusion process, noise distribution is inversely transformed into target data distribution by utilizing a learnable Markov chain in a reverse denoising process, and the target data distribution is subjected to data enhancement. Reconstruction of synthetic data from a random sample with known distribution is realized; combining the original data with the synthetic data to construct an extended training set; and training a regression model for inverting the physiological and biochemical components of the plant by using the extended training set. According to the method, the synthetic samples highly similar to real sample distribution can be generated, and richer data support is provided for a regression model, so that the prediction performance is enhanced.

The invention provides a classifier combination method based on variational Bayesian inference. The method comprises the following steps: respectively training at least two base classifiers through pre-labeled disease attack data; building a probability model by taking the real label of the disease attack data, the confusion matrix of the base classifier and the category prior as hidden variables, and outputting a posterior probability; independently optimizing the posterior probability through a variational inference method to obtain convergent variational distribution output; and obtaining a final disease label according to variation distribution output. According to the method, adaptive fusion of base classifier output is realized through probability modeling and variational inference technologies, and the reliability and efficiency of classifier combination are improved.

This application provides a runoff prediction method, apparatus, electronic device, and computer-readable storage medium. The method includes: acquiring the forecast meteorological time series of a target watershed during the prediction period, historical meteorological time series, and historical runoff time series for historical periods; inputting the historical meteorological time series and historical runoff time series into an attention model to extract global contextual features of the target watershed; inputting the forecast meteorological time series, global contextual features, and initial noise data into a conditional diffusion probability model, performing multiple backdiffusion processes to obtain multiple predicted runoff time series of the target watershed during the prediction period; and calculating a specified quantile for each moment in the prediction period based on the multiple predicted runoff time series to construct a confidence interval, thereby obtaining runoff prediction information containing a risk probability distribution. This method avoids gradient vanishing when processing long-sequence data and outputs the probability distribution of the prediction results.

A probability model may be selected based on an indication of whether a magnitude symbol of a block vector difference (BVD) matches a magnitude symbol of a BVD predictor. The determined probability model may be used to decode an indication of whether other amplitude symbols of the BVD match other amplitude symbols of the BVD predictor. A magnitude of the BVD may be determined using a value of the magnitude symbol of the BVD predictor and the indication of whether the magnitude symbol of the BVD matches the magnitude symbol of the BVD predictor.

The invention discloses a motion alignment method based on state-dependent Bayesian group filtering, which is used for solving the problems of rapid attitude change and state-dependent noise in high-dynamic initial alignment of a strapdown inertial navigation system. The method has two creative points. The method comprises the following steps: firstly, establishing an accurate initial alignment model containing an inertial sensor error by utilizing Lie group representation; an observation probability model is established by adopting a shaft angle model which is more in line with physical definition, and the rotation of the carrier is described and tracked more accurately. Secondly, in the process of designing a state correlation Lie group Bayesian filtering algorithm, exact composition of state correlation noise in the alignment model is deduced and analyzed, and observation noise and the state are decoupled through vector dot product and cross product; experimental results show that the method is obviously superior to the existing method in alignment precision and time.

A method for dependency task prioritization is disclosed. The method includes providing base data including task identifiers, failure values, and dependency data values, where each dependency data value is associated with a pair of tasks. The method also includes generating a probabilistic model using a directed acyclic graph, where each task is associated with a network node and each dependency data value is associated with a network edge of the directed acyclic graph, where each dependency data value represents a conditional probability related to a respective per-time-unit failure rate. Additionally, the method includes determining, for each task in the directed acyclic graph, a posterior marginal probability value representing a probability of the task experiencing a failure, and selecting, based on the posterior marginal probability values, a sequence of tasks that are most likely to fail the fastest.

The invention discloses a method and a system for predicting migration and blockage of fracture particles based on machine learning, and the method comprises the following steps: firstly, presetting fracture roughness coefficient (JRC), flow velocity, equivalent particle size of particles and particle quantity data, constructing a fracture model with a real shape, and carrying out a visual migration test; recording fracture blockage label data; inputting the parameters and the labels into a neural network dichotomy probability model taking a multi-layer perceptron as a core, and training to obtain a blockage probability prediction function; on the basis of the trained model, sensitivity analysis and feature importance evaluation are carried out, the influence sequence of the blockage probability on JRC, the flow velocity, the equivalent particle size of particles and the particle number is output, and prediction of the particle blockage event in the fracture is achieved; according to the invention, the particle blocking mechanism in the crack under the multi-factor coupling effect is researched, and a scientific basis is provided for design and construction of geotechnical engineering.

The invention relates to the technical field of day-ahead electricity price prediction, in particular to a day-ahead electricity price prediction method, device and equipment based on a diffusion model and a computer readable storage medium, and the method comprises the steps: obtaining day-ahead market transaction data, real-time transaction data and auxiliary information in a fixed time period every day, and writing the data into a market information database; based on the market information database, aligning the market information database according to unified time granularity to obtain a multi-dimensional condition feature sequence; inputting the multi-dimensional condition feature sequence into the trained de-noising diffusion probability model, and generating an electricity price prediction sample set at discrete time points in the next day through reverse multi-step Markov de-noising sampling; and obtaining a statistical value of the electricity price prediction sample set at each time point, constructing a prediction curve, taking a preset quantile to obtain a probability interval, and writing a result into prediction data and an evaluation database. Probabilistic generation is carried out on the day-ahead electricity price by adopting a diffusion model, and the prediction precision in an extreme fluctuation scene is remarkably improved.

This invention discloses a spacecraft pose estimation and uncertainty modeling method based on intrinsic space, belonging to the field of spacecraft pose estimation technology. The method involves directly constructing a hierarchical Bayesian probability model on the rotating manifold SO(3) and translation space, using Fisher and Gaussian distributions respectively, and introducing conjugate priors to achieve the fundamental decomposition and quantification of accidental and cognitive uncertainties. An end-to-end multi-task neural network is used to jointly learn the pose probability model parameters, key points, and segmentation information, and an iterative optimization module is employed to improve estimation accuracy. During the training phase, marginal negative log-likelihood and evidence regularization are jointly optimized; during the inference phase, the probability distribution of pose prediction is obtained through analytical marginalization, and the two types of uncertainty are distinguished. This invention improves pose estimation accuracy while outputting well-calibrated uncertainties, providing a reliable basis for the autonomous and safe operation of spacecraft in orbit.

The invention discloses an adjustment capability evaluation and improvement method, system and device based on new energy and energy storage cooperation and a medium. The adjustment capability evaluation and improvement method comprises the following steps: constructing a probability density function and a confidence interval of adjustment capacity based on a new energy output probability prediction model; a probability density function of capacity adjustment is remodeled through an energy storage cooperation strategy, and a confidence interval is narrowed; constructing a joint probability model of the adjustment instruction and the adjustment capacity; and deriving the probability distribution of the adjustment precision and the shortest high-density confidence interval according to the joint probability model. According to the method, the probability distribution of the new energy adjustment capacity is actively remodeled and optimized by using the energy storage system, the confidence interval width of the adjustment capacity is effectively narrowed under the same confidence level, and the improvement effect of energy storage on the new energy adjustment precision is accurately quantified.