166 results about "Confidence interval" patented technology

The invention provides a cigarette abnormal flow standard risk prediction method and system. The method specifically comprises the steps of collecting multi-source data to construct a causal feature set, generating a causal graph according to domain knowledge and algorithm mining, quantifying an average processing effect through a dual machine learning method, constructing a risk model to position a root cause, simulating risk change after intervention through anti-fact inference, and outputting a result. Through the dual machine learning and anti-fact inference technology, the causal effect is accurately quantified, the intervention effect is simulated, the confidence interval is dynamically adjusted, and the risk prediction and decision support capability can be remarkably improved.

The invention relates to a fault early warning method, device and equipment for an energy storage system. The method comprises the following steps: acquiring target data of a target parameter; the target data is generated by preprocessing real-time operation data and real-time environment data of the energy storage system; extracting a target feature corresponding to each target parameter based on the target data; calculating a feature confidence interval of each target parameter based on the historical data of the target parameters, and marking suspected abnormal features based on the feature confidence intervals; identifying at least two fault types based on the suspected abnormal features; based on the current environment data, the load power of the energy storage system and the historical operation and maintenance data of the energy storage system, carrying out fuzzy reasoning on the risk membership degree corresponding to each fault type and dynamically adjusting the basic weight coefficient corresponding to each fault type; and determining a comprehensive risk index based on the risk membership degree corresponding to each fault type and the dynamically adjusted dynamic weight coefficient, and performing fault early warning analysis processing based on the comprehensive risk index. The method can improve the accuracy of fault early warning.

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention discloses an interpretable multi-dimensional CI index dynamic scoring and risk assessment method, and belongs to the technical field of compliance risk assessment. According to the method, firstly, multi-source heterogeneous compliance data are collected, cleaned, subjected to caliber alignment and subjected to quality evaluation, and standardized data are obtained; a hierarchical index semantic system is constructed based on the standardized data, and a core index set is obtained through multi-dimensional screening; then, constructing a multi-dimensional dynamic threshold, calculating a weighted score in combination with the core index set, quantifying a score confidence interval through a Bayesian confidence quantification model, and mapping a risk level to form complete score data; and finally, carrying out interpretability analysis on the complete scoring data, and generating hierarchical interpretation texts and visual display contents. According to the method, systematization, precision and transparency of compliance risk assessment are realized, the suitability and credibility of a scoring result are improved, and reliable support is provided for compliance decision making.

The invention discloses a five-directional stressometer monitoring data anomaly identification method based on PCA confidence interval analysis, and relates to the technical field of hydropower engineering. According to the method, circle centers (zt1, zt2) and radiuses r1r2 of two-dimensional coordinate data points in each time period are obtained, all the circle centers are subjected to arithmetic average to obtain a final circle center, all the radiuses are subjected to arithmetic average to obtain a final radius, and therefore a global confidence circle is formed; and when new data appear, performing the same PCA transformation and coordinate mapping on the new data, and judging whether a new point is in the final confidence circle so as to realize anomaly detection. According to the method, data dimensionality reduction is performed on data of each monitoring point of the five stress meters by adopting a principal component analysis method, and a final confidence circle is formed by performing arithmetic averaging on two-dimensional circle center coordinates and radiuses of monitoring data in multiple periods; judging whether the data of each monitoring point is normal or not by judging whether the circle center coordinate of the new confidence circle is in the final confidence circle or not; the whole method is suitable for hydropower engineering dam monitoring, equipment diagnosis and data analysis scenes of rapid anomaly detection.

The power system inertia prediction method based on the variational Bayesian attention normalization stream comprises the following steps: constructing a system inertia data set; preprocessing the system inertia data set, and dividing the preprocessed data set into a prediction set and residual data according to a time range; respectively generating Q, K and V by adopting variational Bayes, carrying out relative position coding on the generated vectors, and carrying out weighted fusion on the Q, K and V after position coding by utilizing a multi-head attention mechanism; inputting the output of the multi-head attention mechanism into an attention normalization flow model, and capturing complex probability distribution of data through reversible transformation; carrying out model training by adopting a self-defined mixed loss function; and performing multiple Monte Carlo sampling on the trained model to obtain a sampling prediction set, calculating a prediction mean value and a standard deviation based on the sampling prediction set to obtain a significance level, and constructing an interval prediction result under a corresponding confidence interval. According to the prediction method, accurate probability prediction of the inertia of the power system is realized.

The invention discloses a new energy uncertainty interval fine modeling method based on ELM-Copula. Comprising the following steps: 1) data reading and preprocessing: obtaining a power prediction value and an actual output value by reading historical operation data of a new energy electric field, performing kernel density estimation on the per-unit power prediction value and the actual output value, and calculating an error absolute value according to the per-unit power prediction value and the actual output value; 2) constructing a dynamic Copula function model; 3) measuring goodness of fit of the model; 4) calculating a confidence interval of a prediction error, analyzing uncertainty of power generation power prediction, and giving a confidence interval of a prediction value; according to the method, a multi-type dynamic Copula model is introduced to construct a dynamic dependency structure between a prediction error and power, the ELM is applied to a post-processing stage of a dynamic Copula prediction interval, a correction coefficient is generated by learning historical deviation characteristics, and an original interval is shrunk, so that the prediction precision and practicability are improved on the premise that the coverage rate is not reduced.

The invention relates to a method for obtaining credible blasting vibration dominant frequency, which solves the problem of dominant frequency prediction under complex geology through EMD-HHT multi-scale decomposition and statistical confidence interval analysis and is difficult to adapt to dynamically changing construction conditions. EMD decomposition adaptively eliminates low-frequency noise caused by nonlinear response of the rock mass, and the signal-to-noise ratio is increased; a main frequency determination strategy is automatically switched through confidence interval width (RW) to adapt to complex geological conditions; 95% confidence coefficient ensures that a dominant frequency predicted value accords with actual energy distribution; compared with a traditional Samsu formula, the main frequency prediction error is reduced by more than or equal to 40%.

The invention discloses a carbon emission dynamic accounting method and system. The method comprises the following steps: collecting production equipment direct emission, logistics carbon emission, social media / satellite remote sensing and other abnormal event data; extracting unit yield carbon intensity and supply chain path sensitivity through principal component analysis; constructing a QUBO model and solving an optimal transportation path by using quantum annealing; training a prediction model in combination with the sudden emission risk index to output a carbon emission value; dynamically updating an emission factor library according to the cleanliness of the power grid, and accelerating generation of a confidence interval by adopting a quantum random number generator; and when the predicted value exceeds the threshold value and the lower limit of the confidence interval is higher than the threshold value, triggering carbon capture, carbon credit purchase or production regulation compensation according to the priority. The system correspondingly comprises a multi-mode acquisition module, a calculation optimization module, a dynamic accounting module and a carbon compensation module. Full-chain real-time carbon tracking and intelligent compensation are achieved, the problems of traditional accounting data isolation and compensation lag are solved, and the industrial carbon management accuracy is improved.

The invention discloses an EMB clamping force prediction method considering multi-source uncertainty, and belongs to the technical field of automobile industry, and the method comprises the steps: obtaining a historical mapping relation between clamping force and control input, building prior distribution, and employing normal distribution as the prior distribution to describe an expected range of the clamping force; the method comprises the following steps: acquiring observation data of clamping force historically collected by a sensor, introducing the observation data into a likelihood function, and setting a relationship between the observation data and real clamping force to be influenced by multi-source noise to generate an error model so as to describe probability distribution of the observation data under specific clamping force by maximizing the likelihood function; on the basis of a Bayesian inference framework, updating the trust degree of the clamping force predicted by the EMB through prior distribution and a likelihood function, and calculating posterior distribution by using known prior information and real-time observation data; and extracting 95% confidence interval and uncertainty index in the posterior distribution, and when the confidence interval or the system entropy value is greater than the uncertainty index, rejecting or optimizing the prediction result of the clamping force predicted by the EMB.

The invention discloses a multi-step time sequence prediction method and system based on double-segmentation conformal prediction. The method comprises the following steps: acquiring new input data; distributing the new input data to a corresponding clustering cluster to obtain a clustering result; extracting information from the recorder according to a clustering result to construct a prediction interval; adjusting the corresponding content according to the prediction interval; wherein a similar trend sequence is vertically classified and clustered, errors of adjacent time steps are horizontally and dynamically combined to optimize window division, over-estimation and under-estimation errors are asymmetrically processed to construct a precise confidence interval, the precise confidence interval is stored in the recorder, and an error set is dynamically updated. By implementing the method provided by the invention, the defects in the prior art can be overcome through a two-dimensional segmentation mechanism, more accurate uncertainty quantization is realized, and the adaptability and accuracy of multi-step time sequence prediction are improved.

The invention discloses a data-mechanism-knowledge combined driving dam safety state self-diagnosis method and system, and the method comprises the steps: obtaining dam operation state data, extracting a state quantity based on a pre-constructed computable evidence graph, and generating a monitoring evidence; performing inversion correction on mechanism model parameters under the priori constraint of the atlas, and generating mechanism evidence; the monitoring evidence and the mechanism evidence are accessed to a probability graph model for joint inference, and arbitration correction is carried out when a conflict triggering condition is met; and carrying out credibility calibration on the diagnosis result by adopting a conformal prediction method based on the historical calibration sample set, and outputting a security state diagnosis conclusion containing a confidence interval. According to the method, the problems of multi-source evidence conflict, mechanism parameter distortion and low diagnosis result credibility are effectively solved, and credible and automatic diagnosis of dam safety is realized.

A method can include accessing data for a number of reservoir sites, where the data include at least property data for reservoir properties; performing a determination, using the property data for the reservoir sites, as to whether the reservoir properties for the reservoir sites are independent; responsive to the determination, implementing a Sobol' indices technique or a Kucherenko indices technique to generate global sensitivity analysis results that include property indices for the reservoir properties; generating confidence intervals for the property indices; and generating a graphical user interface for rendering the property indices with the confidence intervals for a number of the reservoir properties.

The invention relates to a method for determining a confidence interval for an estimated position of an object and a method for training a physics-informed neural network for this purpose. The invention further relates to a computer program, a device, and a storage medium for this purpose.

The application provides a debris cloud disintegration timing inversion method based on space-time intersection feature statistics, belongs to the field of disintegration timing inversion, and solves the problem of how to improve the accuracy of disintegration timing determination; the method comprises the following steps: traversing the standardized debris cloud orbit data after orbit propagation, calculating the closest distance time of each two pieces of debris; using an Epanechnikov kernel function to perform non-parametric probability density estimation on the closest distance time; combining the inverse function of the cumulative distribution function with the significance level to construct a confidence interval, and removing the debris outside the confidence interval; traversing the debris in the confidence interval, calculating the closest distance time of each two pieces of debris; using an Epanechnikov kernel function to perform non-parametric probability density estimation on the closest distance time; and taking the closest distance time corresponding to the maximum probability density estimation value as the disintegration time of the space target; the application has strong universality and strong robustness.

The application discloses a brain function state evaluation and psychological disease auxiliary analysis system based on a brain fluctuation map, comprising: a multi-modal electroencephalogram signal acquisition and preprocessing module; a brain fluctuation map feature extraction module; an individualized baseline modeling module; and an adaptive weighting evaluation module based on an attention mechanism, which constructs an individualized deviation vector and adaptively weights the current features and deviation features based on an attention network, and outputs a disease risk probability and a channel contribution weight map. Compared with traditional population norms or simple arithmetic means, the method can more truly reflect the stable ground state of the subject itself, effectively eliminate individual differences and measurement noise, and provide accurate dynamic reference system for subsequent abnormal deviation judgment. Meanwhile, the fluctuation threshold calculation unit sets an adaptive threshold based on a statistical confidence interval, and introduces an abnormal deviation index and a multi-dimensional joint determination, further reducing the false positive risk.

The invention is suitable for the technical field of data processing, and provides a time series data anomaly detection method and device, electronic equipment and a program product. The method comprises the following steps: determining target historical data and target real-time data of target time sequence data; training to obtain a trend prediction model according to the target historical data, and determining a trend prediction value and a first confidence interval corresponding to the target time sequence data through the trend prediction model; according to the trend prediction value and the target real-time data, training to obtain a residual prediction model, and determining a residual prediction value and a second confidence interval corresponding to the target time sequence data through the residual prediction model; determining a target confidence interval according to the trend prediction value, the residual prediction value, the first confidence interval and the second confidence interval; and performing anomaly detection processing on the target time sequence data according to the target confidence interval. According to the method, the time series data anomaly detection is performed through the historical data and the real-time data of the time series data, so that the accuracy of the anomaly detection of the time series data is improved.

This invention discloses a method and system for cable line condition assessment and maintenance decision-making, relating to the field of power system technology. The method includes: acquiring cable condition data and determining missing data information; the missing data information includes the location and type of the missing data; based on the missing data information, employing a corresponding data completion strategy to complete the cable condition data, obtaining completed values ​​and corresponding uncertainty indicators; inputting the completed values ​​and corresponding uncertainty indicators into a pre-constructed fault prediction model to obtain the cable's fault probability and confidence interval within a specified future time window; and determining the optimal maintenance strategy based on the fault probability. This invention enables accurate assessment and scientific decision-making regarding cable line conditions even with incomplete information.

This invention belongs to the field of petroleum exploration technology, specifically disclosing a formation pressure prediction method and system based on PSO-CNN-RF-ABKDE. The method includes: collecting logging data of deep abnormal pressure zones in the target well and adjacent wells, regional geological data, and measured pore pressure data from adjacent wells; preprocessing the acquired data; constructing a PSO-CNN-RF model; quantifying the uncertainty of the n-value prediction using the adaptive bandwidth kernel function density estimation ABKDE algorithm; outputting the n-value confidence interval by fitting the residual probability distribution; classifying the formation pressure risk level by combining the general pressure coefficient threshold for deep abnormal pressure; and substituting the n-value confidence interval into the PSO-CNN-RF model to obtain the predicted formation pressure result and mapping it to the specific formation pressure risk level. Compared with existing technologies, this invention achieves a direct mapping between prediction results and risk levels, quantifies uncertainty while clarifying control requirements, and provides a full-chain technical support of "prediction-quantification-control" for drilling safety in deep abnormal pressure zones.

The invention discloses a TFT-LCD residual life probability prediction method fusing physical features and Gaussian process regression, and the method comprises the steps: S1, constructing a multi-source feature data set which comprises performance monitoring data, structure response feature data and environment stress data; s2, constructing a Gaussian process regression model with time and environmental stress as input and performance degradation amount as output, and initializing hyper-parameters of the model by using the multi-source feature data set; s3, adopting variational Bayesian inference to realize online updating and uncertainty quantification of model hyper-parameters; s4, based on the updated Gaussian process regression degradation model, obtaining predicted distribution of performance degradation values at future time points; and S5, based on the prediction distribution, generating a probability density function of the residual life and prediction intervals under different confidence levels through Monte Carlo simulation. The method provided by the invention overcomes the defect of insufficient prediction precision of a traditional method under a small sample condition, and can provide individualized life prediction with a confidence interval.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes such as financial science and technology and medical health, and discloses an analysis method, device and equipment based on distribution alignment and a medium, and the method comprises the steps: obtaining historical business data and reference analysis label data, constructing reference probability distribution, and training an analysis model to obtain an optimization model; and inputting the to-be-analyzed business data into the optimization model to generate target analysis probability distribution, calculating a statistical feature value and a confidence interval, and generating an analysis report containing abnormal prompt information based on the statistical feature value and the confidence interval. According to the method, distribution alignment is carried out on the model by referring to probability distribution, so that the model outputs probability distribution with group judgment features; providing result credibility expression through statistical feature values and confidence intervals; the interpretation and reliability of an analysis report are enhanced through abnormal prompt, and the stability and practicability of an intelligent analysis system in an uncertain scene are improved.

The invention belongs to the technical field of electric power meteorology, and provides a medium and long term wind and light resource prediction method and system considering climate remote correlation factors, and the method comprises the steps: obtaining global climate index historical data, and carrying out the standardization processing of the data, and obtaining a standardized climate index; for the historical resource sequence of the target station, calculating the mutual information value of the standardized climate index, and constructing a forecasting factor set by taking the lag time corresponding to the maximum value of the mutual information as the optimal early warning window period; extracting wind and light resource measured data of the same period in historical years to construct a reference probability density function; and taking the reference probability density function as prior distribution, combining a preset dynamic mode prediction result as a likelihood function, and outputting a prediction result containing a deterministic value and a confidence interval. By means of the characteristic that ocean signals change slowly, the method breaks through the 15-day prediction limit of an atmospheric mode, effective trend prediction from the quarterly level to the annual level is successfully achieved, and the requirement for medium and long term resource evaluation is met.

The invention relates to the technical field of artificial intelligence assisted medical treatment, and discloses a multi-modal help risk assessment method based on order-preserving calibration, and the method comprises the steps: mapping the multi-modal data of a to-be-tested sample to a joint risk measurement manifold space, and carrying out the fusion; then, a clinical semantic anchor point sequence subjected to order-preserving constraint is used as a reference system, and a modal conflict vector is calculated to quantify semantic inconsistency between modals; perturbation is applied to the anchor points based on the conflict intensity to generate an anti-fact anchor point cloud, and the pairwise dominant probability of the to-be-tested sample relative to the virtual anchor points is calculated through a constructed differential partial sequence discriminator; and finally, performing statistical aggregation to obtain a partial order dominance degree sequence, and defining a dynamic risk confidence interval. According to the method, through explicit modeling of modal conflicts and construction of dynamic calibration boundaries, the problem of assessment uncertainty caused by heterogeneous data is solved, risk underestimation is effectively avoided, and an auxiliary diagnosis basis with high robustness and interpretability is provided for clinical decision making.

The invention provides a basin structure parameter inversion method and system based on proxy model optimization and a medium, and the method comprises the steps: obtaining the structure-stratigraphic section observation data of a target basin, and building a geometry-equilibrium-rheology coupled flexural cantilever forward modeling model; initial sampling is carried out in the parameter space, an initial training set is constructed, and a mapping relation between parameters and target function values is established by utilizing Gaussian process regression; based on a Bayesian optimization framework, selecting a to-be-evaluated parameter combination through an expected improved acquisition function, calling a forward modeling model to calculate and update a training set, and iterating until convergence; and finally, importance resampling is carried out based on the trained proxy model, and uncertainty quantification and confidence interval output of inversion parameters are realized. The problems that a traditional method is high in calculation cost, slow in convergence, unable to quantify multiplicity and insufficient in forward modeling physical coupling are solved, and the efficiency and the result reliability of extended basin construction parameter inversion are remarkably improved.

The present invention relates to a method and device for determining parameters influencing the infrared characteristics of a ship. The method comprises: pre-establishing a relationship equation between effective infrared radiation data of each facet on a ship target, multiple environmental parameters, and multiple parameters to be determined; obtaining n*m groups of test data for the corresponding facet of the ship target in an actual scenario; inputting each m groups of test data into the relationship equation to obtain a group of nonlinear constraint equations with multiple parameters to be determined as variables; solving each group of nonlinear constraint equations using the Newton method to obtain a corresponding group of estimated values ​​of the parameters to be determined; calculating a confidence interval for each parameter to be determined based on the n groups of estimated values ​​of the parameters to be determined, determining the estimated value of each parameter to be determined that falls within the corresponding confidence interval among the n estimated values ​​of each parameter to be determined, and determining the target value corresponding to the parameter to be determined based on the estimated value of each parameter to be determined that falls within the corresponding confidence interval among the n estimated values ​​of the parameter to be determined. The present invention can improve the accuracy of the parameters to be determined.

The invention relates to a method for checking the trueness of synthetic training data of a machine learning model, comprising the steps of: providing synthetic training data, the synthetic training data being described by statistical variables, the synthetic training data imitating sensor data, and in the range of training the machine learning model, determining the trueness of the synthetic training data; the upper limit of the confidence interval of the statistical variable is determined based on the synthetic training data, real data is provided, the real data is also described by the statistical variable, the real data comprises sensor data, the sensor data is generated by detection of at least one sensor, and the upper limit of the confidence interval of the statistical variable is determined in the range of reasoning of the machine learning model. A lower limit of a confidence interval of the statistical variable is determined on the basis of the real data, the lower limit is continuously determined from the beginning of the reasoning, the authenticity of the composite training data is checked on the basis of a comparison of the continuously determined lower limit with the determined upper limit, and a system deviation of the composite training data relative to the real data is detected. The invention also relates to a computer program, a device and a storage medium.

The present invention discloses a method and device for determining traffic relationships based on mean trend and confidence interval analysis, belonging to the field of network security technology. The method comprises the following steps: establishing a flexible determination mechanism controlled by trend and confidence during the communication traffic correlation determination process; the flexible determination mechanism controlled by trend and confidence specifically includes: using the mean change trend as the modeling object, using the correlation value of each round to perform real-time updating and fitting of the correlation rolling mean sequence, combining the convergence value and the confidence interval offset direction, dynamically determining whether a conclusion is sufficient, and achieving early termination of the determination process. The present invention improves determination efficiency and response speed, and enhances adaptability and practicality.

The invention relates to the technical field of earthquake monitoring, in particular to an intelligent earthquake monitoring system based on the Internet of Things. The adaptive optimization module is used for dynamically generating an aftershock probability density field; the adaptive optimization module is used for configuring a stress release relaxation factor based on a sequence active modal index calculated by a kurtosis characteristic value and a skewness characteristic value of probability space distribution in a current time slice to obtain a primary iteration aftershock probability density field; the structure-oriented constraint adjusting module is used for adjusting the structure-oriented constraint weight based on the deviation degree of the main axis direction of the high-probability aftershock cluster and the fault trend of the known region to obtain a secondary iteration aftershock probability density field; and the aftershock risk assessment module is used for determining whether the reliability of the secondary iteration aftershock probability density field is qualified or not based on the width of the confidence interval of the aftershock occurrence rate, and optimizing the preset active index based on an unqualified condition. The aftershock probability prediction precision is improved.