16 results about "Bayesian formulation" patented technology

The invention provides a data processing method and device based on clinical test data, in the application, a prior probability and a historical likelihood of a symptom corresponding to target clinical test data can be determined through a historical clinical database, the prior probability reflects a basic epidemic rate of an indication in a population, and the historical likelihood of the symptom corresponding to the target clinical test data can be determined through the historical clinical database. According to the historical likelihood, association rules between symptoms and indications are mined from historical cases, the association rules and the indications are dynamically updated through a Bayesian formula, an inference chain conforming to clinical logic is formed, then, a complex multi-feature joint probability estimation problem is converted into product calculation of single-feature statistics through conditional independent assumption, and a probability estimation result is obtained. The problem of calculation feasibility under high-dimensional data is solved, probabilistic output provides a quantitative basis for auxiliary determination of indications, a data-driven statistical rule is converted into a clinically understandable auxiliary support tool, and objectivity, consistency and scientificity of diagnosis decisions are effectively improved.

This invention discloses an adaptive reclosing method, system, and electronic equipment for power transmission lines in winter based on evidence theory fusion. The method includes calculating the posterior probabilities of different subdivided fault causes under corresponding weather characteristics during winter power transmission line faults using a Bayesian formula model. These probabilities are then used as weights and incorporated into the fault cause identification and classification results output by a pre-constructed waveform image recognition model. Major fault categories are further subdivided to obtain the identification probabilities of different subdivided fault causes. The posterior probabilities and identification probabilities are fused using a D-S evidence theory fusion model to obtain the fused probabilities of different fault causes. Based on these probabilities, the reclosing operation mode, reclosing time, and number of reclosing operations are dynamically adjusted. Compared to existing technologies, this invention significantly improves the accuracy of fault cause identification and enables the reclosing system to dynamically adjust according to weather changes and fault analysis, providing effective decision support for adaptive reclosing of power transmission lines in winter.

The invention discloses a new energy power generation prediction error analysis method and system considering meteorological conditions, and belongs to the technical field of computer data processing and prediction.The new energy power generation prediction error analysis method includes the steps that new energy power generation historical data, prediction error data and meteorological data are obtained and preprocessed, an initial data set is generated, and the statistical magnitude of prediction errors is calculated; combining the meteorological data in the initial data set, using a kernel density estimation method to estimate the joint probability density of the meteorological data and the prediction error data, generating joint probability density distribution, using a Bayesian formula to calculate the conditional probability distribution of the prediction error under a preset meteorological condition, and generating a conditional probability model; and performing multi-dimensional conditional probability modeling on the conditional probability model based on the climate and the position to generate a multi-dimensional conditional probability model. According to the method, kernel density estimation and the Bayesian theory are combined, and multi-dimensional space-time factors are fused to carry out refined modeling, so that the uncertainty of new energy power generation prediction can be accurately quantified, and prospective risk early warning can be realized.

The present application relates to a kind of CVT error evaluation method, device, error evaluation equipment and storage medium, belong to mutual inductor error identification technical field, wherein, the CVT error evaluation method includes: based on the voltage proportionality coefficient of multiple CVTs, first data set is restored to primary voltage level and obtains second data set, and difference matrix is constructed based on second data set;Based on difference matrix, likelihood function is constructed, and likelihood function is converted into posterior probability density function based on Bayes formula;From second data set, the data corresponding to the data of the extraction of the preset number of CVT is constructed third data set, and prior probability density is determined based on third data set;Based on the multi-chain MCMC algorithm of fusion plant rhizome growth and posterior probability density function, when the posterior probability density maximum, the error value corresponding to multiple CVTs is determined.The present application effectively guarantees the accuracy of CVT error evaluation, also improves the efficiency of error evaluation.

The invention relates to a method and equipment for tracing and repairing water conservancy data quality abnormity, and a medium, and belongs to the technical field of intelligent water conservancy. Collecting multi-source water conservancy data, extracting data features and generating a standardized feature vector set; constructing a three-layer Bayesian network topological structure of circulation link-business influence factor-anomaly type, training to obtain a dynamic weight Bayesian network model adaptive to the water conservancy scene, inputting the standardized feature vector into the dynamic weight Bayesian network model obtained by training, outputting quality anomaly root cause probability distribution, and obtaining the quality anomaly root cause probability distribution. The posterior probability of each abnormal root cause is calculated through a Bayesian formula, and a root cause output traceability result is judged; performing graded repair and verification according to different traceability results; and the repaired qualified data is synchronized to a data asset module of the water conservancy credible data space for service output and generation of a visual data quality report. According to the method, the traceability precision is remarkably improved, and the service adaptability is higher.

The application discloses a hydrogen energy storage system electrolytic cell selection method for complex multiple working conditions, comprising the following steps: according to existing large number platform data statistics results, determining different renewable energy hydrogen production scene probability distributions of various regions, multiple type electrolytic cell use probabilities and multiple type electrolytic cell use probabilities under different scenes, taking the data as input (prior probability matrix) of an electrolytic cell selection decision system, and then calculating electrolytic cell use probability matrix (posterior probability matrix) under different application service working conditions through a Bayesian estimation method according to the known prior probability. According to the dynamic correction of the Bayesian formula, the use probability of different types of electrolytic cells under different hydrogen production scenes is corrected, the multi-subject matching characteristics of the renewable energy hydrogen production and energy storage system are effectively ensured, the distribution problem of maximum resource utilization and maximum economic benefit in the renewable energy hydrogen production system is solved, and the economic construction of the hydrogen energy storage system applied to multiple types of scenes in the future can be effectively guaranteed.

The application discloses a kind of methods for measuring the sound reflection coefficient of material in acoustic tube, which comprises the following steps: firstly, the sample to be tested is placed in the test environment to emit and collect signals, and discrete Fourier transform is carried out to establish the sparse matrix form of sound receiving signal; then, the Bayesian formula containing hyperparameters is determined, and the hyperparameters are recursively solved to obtain the estimation result of multipath time delay; then, single-frequency PCW signal is emitted and collected, and the least square method is used to solve the amplitude parameter to obtain the estimation result of optimal multipath amplitude; finally, according to the estimation result of multipath time delay and the estimation result of multipath amplitude, the incident direct wave and the first reflected wave are extracted and separated, and the reflection coefficient of the sample to be tested is calculated. By using the sparse Bayesian learning method and the least square method, the application realizes the time delay estimation and amplitude estimation of multipath signal in acoustic tube, and achieves the purpose of separating multipath signal and calculating the reflection coefficient of acoustic material.

The invention discloses a rotary table multi-source data reliability prediction method based on the Bayesian theory, and belongs to the technical field of numerical control machine tools. The method comprises the following steps: constructing a mechanism reliability model based on geometric errors; according to the constructed mechanism reliability model, obtaining prior distribution and a joint prior probability density function matched with the overall Weibull reliability model under least square method line fitting; a turntable precision degradation test is carried out, and a likelihood function of life data is established according to an overall Weibull reliability model; substituting the likelihood function and the joint prior distribution probability density function into a Bayesian formula to obtain a posteriori distribution probability density function; and estimating parameters by adopting an MCMC sampling method, and carrying out reliability prediction analysis on the worm gear turntable under the condition of fusing different samples. According to the method, the prior information of the mechanism reliability model can be fully utilized under the condition that the service life sample size is small, and the reliability prediction precision in the operation process of the rotary table is effectively improved.

The invention provides a power transmission line fault reason identification method, system and device and a medium, and relates to the field of power systems, and the method comprises the steps: obtaining fault data and a to-be-identified fault, and processing the fault data and the to-be-identified fault to obtain discrete cause information; constructing posterior probability distribution of various fault causes under a given cause condition based on historical discrete cause information; fusing the posterior probability distribution into a preset Bayesian model to obtain a fault identification model; and different impedance angle information is acquired, and the fault identification model predicts and obtains a prediction category based on the impedance angle information. The method is used for identifying power transmission line faults caused by six reasons of lightning stroke, foreign matters, windage yaw, icing, pollution flashover and forest fire. Based on the Bayesian formula, the fault condition probability is calculated by using the fault cause information of the existing fault case and the impedance angle of the fault waveform, and the real fault cause is obtained.

The invention discloses a single-base MIMO radar multi-target DOA estimation method, and belongs to the field of signal processing. According to the method, a Shannon information theory method is combined, and a joint conditional probability density function of a received signal of the MIMO radar, a target direction angle and a target reflection coefficient is deduced by utilizing the property of noise in the received signal; according to a Bayesian formula, firstly, a posterior probability density function of a single-base MIMO radar multi-target reflection coefficient based on a uniform linear array is derived, and a posterior probability density function of a multi-target direction angle when a signal is received is further obtained by combining a derivation result. The azimuth angles of a plurality of targets can be obtained at the same time by performing spectral peak search on the posterior probability density function of the target direction angle. The invention provides a DOA (direction of arrival) detection method based on a posterior probability density function and aiming at the condition that a plurality of targets are densely distributed. According to the method, the posterior probability density function used for uniform linear array monostatic MIMO radar multi-target DOA estimation is deduced based on the information theory, all available information is organically combined through the Bayesian theory, and therefore more comprehensive angle estimation is provided.

The present application relates to the technical field of data verification, especially relates to a kind of distributed photovoltaic irradiation data verification and correction method.The method first obtains grid-connected power station operating data, and the actual irradiance of each point is deduced based on photovoltaic module physical model;Subsequently, the reference point is screened from multiple dimensions, and the total deviation of measured and original simulated irradiance is decoupled into regional system deviation factor, local shading deviation factor and installation equipment deviation factor.On this basis, combined with historical meteorological statistics experience, the prior distribution of candidate point correction factor is established, the posterior mean and variance of each point correction factor are calculated by using Bayes formula to fuse measured likelihood information.Finally, the original data is calibrated using the expected value of correction factor, and the confidence interval under the preset confidence level is output.

This invention relates to the field of coating process monitoring and analysis technology, specifically to a coating process anomaly prediction system and method. The method includes: acquiring the core processes of coating production; establishing a data flow model between the core processes based on a multiple linear regression model; optimizing the regression coefficients of the model using Gaussian process regression combined with particle swarm optimization to determine the optimal regression coefficients; performing anomaly propagation path inference based on a Bayesian network path probability inference model; determining the edge weights of directed edges between nodes based on the calculation results of the improved Bayesian formula, and determining the influence coefficients between processes; processing the influence coefficients between processes, determining the predicted abnormal process for each process when an anomaly occurs, and sending notification information. This invention achieves accurate anomaly prediction and efficient notification, enhancing the accuracy and targeting of anomaly prediction, and is more suitable for the anomaly prevention and control needs of complex coating production processes.

The invention relates to a weather forecasting method, in particular to a rainfall level integrated forecasting method based on a Bayesian formula and an evidence theory. The invention aims to provide a rainfall level integrated forecasting method based on a Bayesian formula and an evidence theory, and provides a firm and reliable technical support for further improving the rainfall forecasting precision and promoting the application of the rainfall forecasting method in flood and drought disaster early warning and water resource scheduling. According to the technical scheme, the rainfall level integrated forecasting method based on the Bayesian formula and the evidence theory comprises the following steps of 1, rainfall forecasting and actually measured data collection; step 2, rainfall level determination; 3, determining and calculating Bayesian formula distribution; step 4, evidence fusion; and 5, integrating forecast evaluation.

The small current grounding system single-phase grounding fault line selection method solves the problem of how to improve the line selection accuracy, and belongs to the field of small current fault line selection. The two power frequency period data before the fault are used as the reference, and the data after the fault are compared by difference, so that the inherent interference such as load imbalance, harmonic background and line parameter asymmetry during normal operation of the system is eliminated, and the fault characteristics are more prominent. Secondly, four criteria (differential steady-state phase, wavelet packet differential energy spectrum, differential instantaneous phase consistency and envelope weighted cumulative polarity) are used to describe the fault from different physical aspects. Each criterion independently outputs a 0~1 confidence level, forming a multi-dimensional complementary verification mechanism. Even if a certain criterion fails under certain conditions, other criteria can still provide reliable evidence. Finally, the prior probability and the four criterion likelihood values corrected by dynamic weight are fused through the Bayes formula to obtain the posterior probability of each branch and bus grounding, and the high and low threshold values are used for hierarchical decision-making.

The embodiment of the invention discloses a coal mine rock burst occurrence probability dynamic speculation method, and belongs to the technical field of coal mine danger prediction, and the method comprises the steps: carrying out the preprocessing of historical monitoring data of a target working face; using a comprehensive index method to calculate and quantify a danger index as the prior probability of Bayesian; dividing the target area into n sub-areas according to a preset step pitch; constructing a prediction system, wherein the prediction system comprises a prior layer and a likelihood layer; setting each monitoring layer of the likelihood layer as a 4-level discrete state, and determining a judgment threshold value of the 4-level discrete state; respectively judging the current discrete state of each monitoring layer of the likelihood layer according to the judgment threshold, and integrating the current states of all the monitoring layers; setting a probability transfer rule of an initial default dynamic state of each monitoring layer state of the likelihood layer; and calculating the posterior probability of rock burst occurrence in each monitoring state based on a Bayesian formula. According to the method, the engineering suitability, the dynamic flexibility and the field pertinence can be improved.

The invention provides a multilevel resource management method and system for communication equipment, and relates to the technical field of power optical communication networks. A resource relation graph is constructed by obtaining multi-layer resource configuration data, nodes represent resource entities, and edges represent connection relations; fusing LLDP, equipment configuration, SNMP and manually recorded multi-source data, and calculating the existence probability of each edge in the resource relation graph through a Bayesian formula to represent the connection uncertainty; receiving a path generation request, converting the existence probability into an edge weight, and searching an optimal path as a candidate service path by adopting a Dijkstra algorithm integrated with engineering constraint pruning; and acquiring an actual network path through routing tracking, calculating a consistency index of the actual network path and the candidate path for verification, and dynamically updating the existence probability of the edge and the weight of the data source according to a verification result to form closed-loop optimization. According to the invention, the accuracy of service path planning and the adaptive capability of the system in a complex network environment are effectively improved.