13 results about "Bayes classifier" patented technology

The invention discloses a fault diagnosis method based on integrated empirical mode decomposition and a manifold structure, and aims to research an algorithm model capable of realizing effective fault diagnosis for an early fault with weak characteristics. The main core of the method is to integrate eigenmode function components obtained by empirical mode decomposition, judge the sensitivity of the eigenmode function components to early faults so as to provide a variable reconstruction strategy more sensitive to the early faults, and meanwhile, extract local features and manifold structures by using a neighborhood preserving embedding algorithm so as to improve the robustness of the early faults. And high-order statistical features more sensitive to early faults are constructed in combination with a statistical local algorithm, so that the high-order statistical features are input into a Bayesian classifier, and finally early fault diagnosis is realized. Compared with a traditional method, the method can more effectively distinguish different types of early faults, obtains higher accuracy, and is a more excellent early fault diagnosis method.

The embodiment of the application relates to an algorithm and a device of a Bayesian classifier of a non-uniform background, which are applied to an underwater active sonar system, the algorithm comprising: obtaining underwater data to be measured and auxiliary data through the active sonar system; the number K of the auxiliary data is greater than 0; modeling classification of the unknown covariance matrix structure into a binary hypothesis testing problem; hypotheses of the binary hypothesis testing problem comprise H i Wherein i=0, 1, H0 is a case that the unknown covariance matrix is a complex conjugate symmetric matrix; H1 is a case that the unknown covariance matrix is a real symmetric matrix; a Bayesian model is set, the Bayesian model comprising a complex inverse Wishart random matrix and a real inverse Wishart random matrix; a classifier for distinguishing the two hypotheses is obtained by using a minimum error probability criterion.

The invention discloses a circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid) interaction prediction system and a circRNA and miRNA interaction prediction method of a graph Fourier pulse neural network. The method comprises the following steps: on the basis of high-throughput sequencing omics data of complex diseases, constructing a heterogeneous biological information network containing drugs, diseases, proteins, circRNA, miRNA and lncRNA; converting the topological features of the entities into a unified feature space by using a graph convolutional network; designing a pulse graph neural network in combination with Fourier coding and a pulse neural network, and extracting a topological structure and high-order semantic features in the network; fusing sequences, topologies and semantic features of circRNA and miRNA through a gate multilayer perceptron to obtain embedding features of circRNA and miRNA; and finally, the interaction of circRNA and miRNA is predicted by adopting a Bayesian classifier. According to the method, heterogeneous biological information is modeled from the perspective of network science, Fourier coding, spiking neurons and graph embedding learning are utilized, the action mechanism of circRNA and miRNA in complex diseases can be disclosed, and the method has good practicability and application prospects in the fields of artificial intelligence, life science, clinical medicine and the like.

This disclosure provides a security detection method, apparatus, storage medium, and electronic device based on a large model, relating to the field of network security technology. The method includes: acquiring training sample emails to train a Bayesian classifier, determining the trained Bayesian classifier; using the trained Bayesian classifier to detect emails to be detected, determining a first detection result; if the first detection result is in a first detection interval, determining structured prompt words based on the first detection result; inputting the structured prompt words into a locally deployed large language model, determining the output result of the large language model; and performing either allow or block actions on the emails to be detected based on the first detection result and the output result of the large language model. This achieves accurate email identification.

The invention relates to an underground water environment risk assessment method based on big data, and relates to the technical field of underground water monitoring and pollution risk assessment. According to the method, water quality parameter data filling is carried out based on a big data pre-trained water quality parameter filling model, a Bayesian classifier or a K nearest neighbor mean value through adaptive missing rate and data type selection, and the calculation efficiency and prediction precision of different missing rates are balanced; a similarity function is defined by considering sampling sites and water quality parameters, repeated data screening is carried out, and data with similar water quality parameters and non-similar sites are prevented from being deleted. Clustering and quartering anomaly mixed detection is utilized to consider univariate statistical anomaly and multivariate structure anomaly, and true pollution data is prevented from being deleted by mistake. A groundwater monitoring data set is utilized, and a composite groundwater environment evaluation model is trained through a differential evolution optimization fusion weight mode to estimate a groundwater environment index value. Model complementation is utilized to reduce deviation; and the integration benefit of the model is maximized by optimizing the weight through differential evolution.

The invention discloses a transformer risk assessment method and device based on multi-index fusion, relates to the technical field of electrical performance test and electrical fault detection, and mainly aims to solve the problem of low accuracy of existing transformer risk assessment. The method mainly comprises the steps that comprehensive risk values of different transformers are calculated according to static risk indexes of the transformers, static risk levels are determined according to the comprehensive risk values, and real-time monitoring data in the operation process of the transformers are obtained according to data monitoring frequencies determined according to the static risk levels; according to the real-time monitoring data, identifying a first state evaluation result of each transformer through a pre-constructed Bayesian classifier, and identifying a second state evaluation result of each transformer through a pre-constructed fuzzy evaluation model; and for any transformer, performing cooperative verification according to the first state evaluation result and the second state evaluation result of the transformer to obtain a risk evaluation result of the transformer. The method is mainly used for evaluating transformer risks.

This invention provides a method for detecting battery thermal runaway anomalies and a battery management unit. During detection, operational data for each battery sub-module is acquired. Based on this data, the corresponding Channenzo entropy or Channenzo entropy change rate is obtained and compared with a set target threshold. When the parameter value in the sample to be detected exceeds the target threshold, it is considered thermal runaway anomaly data. Simultaneously, a Bayesian classifier is used for further data processing, and the abnormal data is output and an alert is issued, thereby effectively improving the accuracy of battery operational data detection and ensuring battery usability and safety performance.

The application discloses a communication modulation identification model training method and device, sensitive feature sets of actually collected communication signals are extracted, the sensitive feature sets are input into a deep neural network model to obtain initial classification results, the signals are input into a naive Bayes classifier, the naive Bayes classifier is initially constructed by using the communication signals, a reward threshold thr is set, a reward function Q(k) is constructed based on the reward threshold thr, the naive Bayes classifier is optimized based on a reward value and a penalty value to obtain a naive Bayes classifier with a weight value, and the weight of the naive Bayes classifier is updated by using a reinforcement learning algorithm. The rough classification results of the deep neural network model are classified again by using the naive Bayes classifier, the naive Bayes classifier is optimized by setting the reward value and the penalty value, the weight of the naive Bayes classifier is continuously updated by using the reinforcement learning algorithm, and the accuracy of the communication modulation identification model in identifying signals is further improved.

This invention belongs to the field of industrial control system safety technology and discloses a method for detecting protective layer failure in petroleum refining units based on multi-source data fusion. This invention obtains a normalized process monitoring data training matrix and a statistical alarm information matrix by normalizing and adjusting alarm levels using data collected from petroleum refining unit simulation software. Following the principle of maximum correlation and minimum redundancy, the variable set for protective layer failure detection and diagnosis is selected. The invention then fuses the protective layer failure detection and diagnosis results obtained from the statistical alarm information data using a discrete Bayesian classifier and from the protective layer failure detection and diagnosis results obtained from the process monitoring data training matrix using a continuous Bayesian classifier, resulting in the final protective layer failure detection and diagnosis results. Compared with existing methods, this invention creatively integrates statistical alarm information data, eliminating the interference of noise in the process monitoring data on the detection and diagnosis of protective layer failure.

This invention discloses a method and system for recommending demand response mechanisms at the feeder level in distribution networks based on Bayesian classification. The method includes: Step 1, randomly acquiring hourly output data of distributed photovoltaic (PV) power and electric vehicle (EV) behavior data from each feeder in the distribution network using the Monte Carlo method, and selecting typical scenario data; Step 2, extracting feeder-level feature attribute data from the typical scenario data, using the feature attribute data as input conditions, and having an improved Bayesian classifier output feeder flexibility category labels, recommending feeder-level demand response mechanisms, including price-based demand response and quasi-linear demand response, to the distribution network operator based on the flexibility category labels; Step 3, constructing feeder-level load quasi-linear variables; Step 4, performing PV-energy storage-EV collaborative optimization scheduling. This invention achieves refined management of the distribution network and collaborative optimization of PV-energy storage-charging systems by intelligently assessing feeder flexibility and constructing feeder-level load quasi-linear variables.

The invention relates to a cardiovascular disease risk cycle assessment method based on big data, and the method is characterized in that the method comprises the steps: obtaining a preliminary diagnosis result through correlation analysis and a support vector machine model according to the basic information and symptom performance of a patient; according to comprehensive information such as electrocardiograms, echocardiograms, blood examination and CT examination, the cardiovascular conditions are classified through a correlation analysis algorithm and a Bayesian classifier. Physiological indexes such as electrocardio, blood pressure and blood fat are monitored regularly, a physiological change period is obtained, and influences of factors such as diet, exercise and emotion on the physiological change period are analyzed. Based on the electrocardiogram period, the blood pressure fluctuation period and the blood fat change period, an ARIMA model is established to predict the lesion period. And further fusing surgical treatment, drug treatment, lifestyle adjustment and psychological treatment schemes, and constructing a recurrence probability and recurrence cycle prediction model by combining basic information and physiological change cycle of the patient. And finally, dynamically adjusting a monitoring period according to a prediction result, and providing a monitoring result and personalized adjustment suggestions for a doctor or a patient through a medical system, thereby realizing periodic and dynamic assessment and management of cardiovascular disease risks.

The application discloses a water quality evaluation machine learning Bayesian model based on suspended particle matter density, and comprises the following steps: collecting and testing the suspended particle matter density indexes and other multiple water quality indexes of a plurality of water quality samples; establishing an entropy weight Bayesian model for water quality evaluation; calculating entropy weight values; calculating the posterior probability P of the samples being in class I to class V water by using the distance formula with the weight; dividing the adjacent grades between class I to class V water quality grades into ten equal parts, and determining the water quality grade according to the difference between the highest probability value and the second highest probability value in the posterior probability P; matching the water quality grade of each sample with the suspended particle matter density of the sample, selecting a large number of samples as a training set, and generating a water quality evaluation Bayesian classifier based on suspended particle matter; the application simplifies the existing water quality rapid evaluation method, improves the accuracy of the evaluation by improving the calculation formula in the prior art, and realizes the rapid evaluation of the water quality grade based on the suspended particle matter density.

The application discloses a wind turbine blade control method, device, equipment and storage medium, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring historical operation state data and historical clearance data of a target wind turbine, and dividing the historical clearance data into a dangerous clearance data set and a safe clearance data set based on a preset dangerous clearance threshold; performing correlation analysis on each dangerous clearance data in the dangerous clearance data set and the corresponding operation state data, and on each safe clearance data in the safe clearance data set and the corresponding operation state data to determine a first training data set and a second training data set; and generating a clearance Bayesian classifier model corresponding to the target wind turbine; and determining the blade clearance state of the target wind turbine according to the clearance Bayesian classifier model and current operation state data to control the operation of the wind turbine blade. In this way, it can be determined whether to start the variable pitch according to the current clearance state of the target wind turbine at any time to protect the safety of the wind turbine blade.