48 results about "Bayes' theorem" patented technology

The invention discloses a Bayesian traffic density estimation method based on data driving in a mixed traffic environment, and the method comprises the steps: S1, building a probability relation model between traffic density and vehicle travel time, and between a vehicle and a front vehicle space-time region area based on measurement data; s2, setting prior probability distribution for the vehicle travel time and the model noise variance; s3, calculating joint posteriori distribution of the vehicle travel time and the model noise variance by using the Bayesian theorem, and performing marginalization processing to obtain marginal posteriori distribution of the vehicle travel time; and S4, probability estimation is carried out on the traffic density of the unobserved space-time region through posterior prediction distribution. The technical problem of traffic density estimation in the mixed traffic environment is effectively solved by establishing a complete Bayesian probability framework, and the estimation precision is remarkably improved by adopting the steps of prior distribution setting, posterior distribution calculation, marginalization processing and the like based on CAV dynamic data acquisition.

The invention provides a biomedical causal relationship inference method and system based on a Bayesian knowledge graph, and relates to the technical field of biomedical data mining and artificial intelligence, and the method comprises the steps: carrying out the multi-source evidence fusion of biomedical data, and obtaining a structured triple containing Bayesian confidence; analyzing the triple by using priori knowledge and obtaining a conditional probability table through parameterized filling; carrying out posteriori updating by using the Bayesian theorem; and analyzing the updated knowledge graph state by using a graph neural network model to obtain an inference result. Wherein the Bayesian inference module is combined with the graph neural network model, the former provides priori knowledge with confidence, the latter provides a fine path dependency relationship, and the accuracy and robustness of inference are remarkably improved. According to the method, the problems of evidence isomerism fragmentation, causal inference subjectization and knowledge discovery inefficiency are solved, and intelligent and automatic inference of the causal relationship is realized.

A rock burst risk dynamic assessment method based on a Bayesian method comprises the steps that a stope working face is divided into M * M regular grids, and each grid serves as an independent assessment unit; collecting geological and mining data, selecting a plurality of influence factors based on historical data and expert knowledge, and quantifying the plurality of influence factors by adopting a static evaluation model; determining weights of a plurality of influence factors by using an analytic hierarchy process, and calculating an initial risk score of each evaluation unit by using a comprehensive index method; normalizing the initial dangerousness score into prior distribution by adopting an S-type function, and constructing an initial dangerousness prior probability field; a power law attenuation model is introduced to calculate influence weights among the evaluation units, and an influence weight matrix is constructed; real-time monitoring data are obtained through a micro-seismic monitoring system, the risk probability of each evaluation unit is updated in real time based on the Bayesian theorem, and dynamic evaluation and evaluation result output are achieved. According to the method, the accuracy and the real-time performance of rock burst risk assessment can be effectively improved.

The invention discloses an SRAF placement method based on a Bayesian model. The method comprises the steps that historical SRAF configuration parameters and photoetching simulation data are collected and preprocessed; based on the preprocessed data, using kernel density estimation to construct prior probability distribution; constructing a likelihood function of multiple photoetching result indexes in combination with a Hopins photoetching model; calculating approximate distribution of a posterior probability through variation inference by using the Bayesian theorem; and finally selecting an optimal SRAF configuration parameter according to the posterior distribution, and verifying the manufacturing feasibility of the process window and the mask. According to the method, historical prior knowledge and real-time photoetching data are organically integrated through the Bayesian model, and high-precision, high-efficiency and high-robustness optimization of the SRAF layout is realized by combining specific technical characteristics such as preprocessing, kernel density estimation, a Hopkinson physical model and variation approximation; the problems of complex rule base, time-consuming calculation and insufficient adaptability in the prior art are effectively solved.

The invention provides a user satisfaction prediction method and device and a medium, and relates to the technical field of computers. The method comprises the following steps: based on a first index, using a user satisfaction model obtained by training according to user complaint data in advance to obtain a first probability that a first user is not satisfied; based on a second index, obtaining a second user who has a poor quality event in the first users, and correcting the first probability of the second user by using the Bayesian theorem to obtain a second probability that the second user is not satisfied; and predicting an unsatisfied fourth user according to the first probability of the third user who does not generate the poor-quality event in the first user and the second probability of the second user. According to the method, the probability that the user is not satisfied according to the user complaint prediction is corrected through the Bayesian theorem according to the poor-quality event, so that user satisfaction evaluation errors caused by the fact that some users are not satisfied with commodities or services actually but do not complaint are avoided.

The invention provides a method for establishing a high-dimensional feature posterior probability estimator based on a sparse Bayesian method. The method comprises the following steps: (1) constructing a sparse Bayesian posterior probability estimation model; (2) model parameter representation based on the Bayesian theorem; (3) performing model parameter solving by using the sample data; and (4) realizing model sparsification based on automatic correlation confirmation. The invention designs a high-dimensional feature posterior probability estimator by using a sparse Bayesian method in order to solve the problem that the feature category affiliation probability is difficult to solve due to the fact that the high-dimensional feature probability distribution is difficult to estimate.

A multi-source heterogeneous data driven large rotating machinery fault diagnosis method, which first collects one-dimensional data composed of low-frequency vibration signals, medium-frequency vibration signals and temperature signals, and two-dimensional data composed of operation images at the same time, then fuses the one-dimensional data and two-dimensional data by Bayes theorem to obtain a diagnosis result, at the same time, obtains a diagnosis result B according to the low-frequency vibration signals and medium-frequency vibration signals, and obtains a diagnosis result C according to the temperature signals, then sums up the diagnosis results A, B and C according to the weighted summation method to obtain the final diagnosis result, if the final diagnosis result is greater than or equal to 0.5, it is judged that there is a fault, if it is less than 0.5, it is judged that there is no fault. The design not only monitors multiple signals, but also has good fault diagnosis effect.

This invention relates to the field of seismic data processing technology, specifically disclosing a method for removing strong reflection shielding in hidden river channels based on adaptive hybrid L0-L1 norm. The method includes: first, establishing an optimization objective function based on Bayes' theorem using hybrid L0-L1 norm; second, dynamically adjusting the L0-L1 norm weights using an adaptive weight function driven by the seismic signal, combined with reflection coefficient amplitude and residual information, enhancing sparsity constraints in strong reflection zones and reducing constraint strength in weak reflection zones; then, constructing a convex upper bound for the objective function using a minimization framework, and solving it iteratively in stages using an accelerated rapid iterative threshold shrinkage algorithm, while incorporating prior knowledge of seismic wave propagation laws and river channel deposition patterns to ensure the geological rationality of the solution. This invention solves the technical problems of traditional sparse processing methods, such as fixed parameters, lack of geological constraints, and inability to simultaneously address strong reflection suppression and weak signal protection, significantly improving the separation accuracy of strong reflections and the recovery rate of weak signals, effectively overcoming the strong reflection shielding effect.

The invention discloses a heading machine hob abnormal state early warning method and device. The method comprises the steps that real-time heading parameter data, hob vibration data, hob infrared video data, hob temperature data and rock slag image data are acquired; acquiring historical tunneling parameter data according to a specified mileage, inputting the historical tunneling parameter data and the real-time tunneling parameter data into a first hob damage early warning model, and outputting a first early warning result; inputting the hob vibration data into a second hob damage early warning model, and outputting a second early warning result; inputting the hob infrared video data and the hob temperature data into a third hob damage early warning model, and outputting a third early warning result; inputting the rock slag image data into a fourth hob damage early warning model, and outputting a fourth early warning result; and based on the Bayesian theorem, fusing the first early warning result, the second early warning result, the third early warning result and the fourth early warning result to obtain a hob state early warning result. The accuracy of early warning of the abnormal state of the hob can be improved.

The invention discloses a Bayesian model averaging-based power prediction model evaluation method and a related device, and belongs to the technical field of power system prediction. The method comprises the following steps: constructing a candidate model set comprising a plurality of power prediction models; secondly, historical data are used for training all the models, the posterior model probability of each model is calculated based on the Bayesian theorem, the posterior model probability serves as the scientific weight of the model, and the goodness of fit and complexity of the model are considered in the weight at the same time; and finally, for a new prediction input, performing weighted average on the prediction distribution of each candidate model by taking the posterior probability as the weight to generate comprehensive probability prediction distribution. According to the method, the advantages and disadvantages of each candidate model are scientifically evaluated through the posterior probability, and a comprehensive and probabilistic prediction result is finally generated, so that the robustness and reliability of prediction are improved, and richer decision information is provided for power grid dispatching.

A meta continual-learning and inferring method uses an implementation of Bayes' theorem, and includes: calculating a likelihood of learning data for a given latent variable by a data distribution learner; performing a sequential Bayesian update and calculating a final posterior distribution of the latent variable by using prior distribution of the latent variable and the calculated likelihood by a Bayes' calculator; sampling the latent variable from the final posterior distribution; and inferring test output data based on the sampled latent variable and test input data by an inference engine, wherein respective meta parameters of a neural network of the data distribution learner, the prior distribution of the latent variable of the Bayes' calculator, and a neural network of the inference engine are trained by a meta learning.

The invention discloses a core component performance prediction method, system and device based on industrial Internet of Things, and a medium, and relates to the technical field. The method is implemented through a five-platform architecture including a user platform, a service platform, a management platform, a sensing network platform and a production object platform, multi-dimensional feature vectors of core components in an operation cycle are obtained in real time, feature alignment processing is executed, a clustering algorithm is adopted to cluster performance degradation modes of the core components, and the performance degradation modes of the core components are obtained. Prototype vectors reflecting different decline stages are generated, a knowledge graph of a performance decline evolution path is established in combination with equipment working condition labels, prediction results are calculated through an inference method of the Bayesian theorem in combination with the knowledge graph, meanwhile, relation edge weights and confidence coefficients are updated according to prediction and actual results, and performance decline of core components can be accurately predicted. The prediction accuracy and reliability are improved, the service life of equipment is prolonged, the maintenance strategy is optimized, and the non-planned downtime and the maintenance cost are reduced.

The invention provides a rapid calculation method for limit clearing time based on an improved Bayesian optimization algorithm, and relates to the field of power systems, and the method comprises the steps: obtaining original parameters of different faults of a power system, and discretizing the original parameters into an integer optimization model; the method comprises the following steps: initializing an exploration domain, sampling to generate an initial data set, constructing a Gaussian process model based on the initial data set, calculating a corresponding acquisition function to obtain a next most potential sampling point, and updating the exploration domain; adding observation data corresponding to the new sampling points into the initial data set, updating a Gaussian process model according to the Bayesian theorem, and repeatedly calculating a corresponding acquisition function until a preset convergence criterion is met or the maximum number of iterations is reached, so as to obtain an optimal model; and calculating the lower limit clearing time of each fault of the power system based on the optimal model. According to the method, the continuous optimization problem is discretized, the exploration interval is dynamically cut in the iteration process, the evaluation number is reduced, and a solution close to the global optimum is quickly found.

The invention discloses a concrete strength springback error probability calibration method based on computer vision, and the method comprises the steps: obtaining original pictures of local concrete at different positions of the surface of a component in an existing concrete building; carrying out a rebound test on the concrete at the image shooting position; core drilling sampling is conducted on the component, and the actual strength value of internal concrete is measured; calculating the error between the concrete strength rebound value and the actual strength of the drill core; training a concrete strength springback error prediction model, predicting prediction errors of concrete springback values and actual values at different positions of the surface of the to-be-detected member, and then calculating strength prediction estimation values of concrete at different local positions; and taking the strength prediction estimation values of the concrete at different positions as noise observation data of the real strength of the component, constructing a normal likelihood function, and deducing the probability distribution of the real strength of the concrete in the component through the Bayesian theorem in combination with the prior strength distribution.

The application discloses a runoff reconstruction method for a glacier-covered area without actual measurement data, relates to the technical field of geographic information and hydrology, and comprises the following steps: acquiring multi-source heterogeneous data of a target basin; extracting an area time sequence of an ice lake based on remote sensing images, constructing an area-water level-storage capacity relationship curve based on a digital elevation model, and determining an ice lake storage capacity change time sequence; constructing an ice hydrology physical model and integrating an ice lake reservoir module based on ice distribution data and meteorological reanalysis forcing data; determining a prior distribution through a cross-basin parameter migration technology, constructing a likelihood function based on the ice lake storage capacity change time sequence, determining an optimal parameter set based on Bayes theorem, and running the ice hydrology physical model to obtain a primary simulated runoff sequence; and performing deviation correction through a correction model based on the primary simulated runoff sequence, the ice lake storage capacity simulation time sequence and an environmental characteristic sequence to generate a reconstructed runoff sequence. The application can improve the accuracy and reliability of runoff reconstruction.

The invention discloses a Bayesian probability fatigue strength evaluation method for a structural member with defects, and relates to the technical field of fatigue strength prediction of aerospace metal structural members. The method comprises the following steps: firstly, drawing a fatigue limit curve based on test data of a defective material sample, embedding a physical rule of an El Hadad curve through a synthetic data set, and establishing physical priori knowledge of a defect size and a fatigue strength behavior; training model parameters by using test data of the structural member, constructing a Bernoulli likelihood function, and matching an actual failure tag of the structural member; and finally, fusing the prior distribution and the likelihood function through the Bayesian theorem to obtain probability distribution after parameter updating, and outputting a predicted failure probability expected value and uncertainty. According to the method, material sample data containing defects are fused as physical prior based on the Bayesian theory, and the probability fatigue strength is evaluated through experimental data of a small amount of structural parts.

The invention provides a vehicle automatic driving control method fusing belief updating and a drift diffusion model, which adopts a pre-trained multi-mode neural network to realize accurate inference of priori belief of pedestrians, then introduces Bayesian theorem and surprise amount calculation, quantifies the difference between vehicle behaviors and priori belief expected by the pedestrians in real time, and improves the accuracy of vehicle behavior prediction. The method solves the problem of insufficient perception of the internal state of the pedestrian, and finally, adopts a pre-trained drift diffusion model to output the decision tendency and decision time of the pedestrian at the same time, provides key time sequence information for vehicle control, solves the safety risk caused by prediction lag, avoids unnecessary parking deceleration, can timely trigger emergency avoidance, and improves the safety of the vehicle. And the traffic efficiency is obviously improved while the safety is guaranteed.

The invention discloses a stochastic simulation method and device based on three-dimensional dynamic spherical model data, electronic equipment and a medium. The method can comprise the steps of obtaining a first expression of a simulation result of target data through a local prior distribution function according to the Bayesian theorem; establishing a second expression according to the first expression; and solving the second expression through Gaussian simulation to obtain a final simulation result of the target data. By dynamically acquiring the data of the three-dimensional spherical model and calculating the expectation value and the variance value of the dynamic data, a more accurate simulation result can be obtained.

The invention relates to the technical field of aquaculture disease prevention and control and intelligent diagnosis, and provides a fish disease accurate diagnosis method based on an expert database and a Bayesian theorem, and the method comprises the following steps: 1, constructing an expert database; 2, constructing an expert type training set, analyzing an original expert knowledge table in an expert type database, and extracting a feature corresponding relation; 3, constructing a Bayesian fish disease diagnosis model, and calculating a posterior probability based on the Bayesian theorem; and 4, performing model verification and diagnosis application. According to the method, on the basis of the constructed expert type training set, disease and symptom data are processed in a structured mode, a standardized feature coding system and a feature relation matrix are established, the inherent frequency of a database and actual distribution of the training set are balanced through a prior probability correction mechanism, and the model can calculate the disease probability more accurately.

A path planning method for multi-unmanned aerial vehicle collaborative search comprises the steps that firstly, an unmanned aerial vehicle kinematics model, a sensor model and an environment cognition map are established, and a target existence probability and environment uncertainty map is updated in real time based on the Bayesian theorem; secondly, constructing a model-based prediction control path decision framework, predicting a future multi-step path through rolling optimization, and generating a control sequence; in the optimization solution, a double-population three-layer competition mechanism is adopted, particle population individuals are divided into a loser, a candidate and a winner, and differential updating strategies are adopted to balance the global exploration and local utilization ability of particles; meanwhile, a feedback mechanism based on particle evolution quality is introduced, the size of the sub-population is dynamically adjusted, and population diversity is maintained in combination with periodic recombination. According to the method, the solving quality of the search path planning problem based on model prediction control is remarkably improved, meanwhile, the high search coverage rate is guaranteed, and the method is suitable for multi-unmanned-aerial-vehicle cooperative search scenes such as disaster rescue and environment monitoring.

The invention discloses an enterprise system execution process monitoring method and device and a medium, and relates to the technical field of system monitoring, and the method comprises the steps: calculating an instantaneous information entropy value of each process node when a new process instance passes through an enterprise system execution process model, comparing the instantaneous information entropy value with a path monitoring baseline, obtaining a first abnormal evidence, and obtaining a second abnormal evidence; collecting the activity time consumption of each process node when the new process instance passes through the enterprise system execution process model, comparing the activity time consumption with the time consumption monitoring baseline to obtain a second abnormal evidence, and calculating the compliance confidence coefficient score of the new process instance by using the Bayesian theorem based on the first abnormal evidence and the second abnormal evidence, and performing comparison based on the compliance confidence score of the new process instance and a preset threshold interval, and executing different levels of management intervention operations according to a comparison result. According to the two-dimensional dynamic compliance monitoring method, the accuracy, the adaptability and the perspectiveness of enterprise system execution monitoring are improved, and the preposition and refined management and control of compliance risks are effectively supported.

The application belongs to the technical field of gas transmission and distribution, and particularly relates to a smart gas pipe network abnormal event positioning and recovery method. The method first acquires pipe network topological structure information and multi-source heterogeneous monitoring data, and constructs a graph structure representation of the gas pipe network; then, time and space features are extracted by using a space-time feature extraction network to fuse time and space features, and an abnormal event type is identified by using an abnormality detection model; then, a probability distribution of an abnormality propagation path is generated by using an abnormality propagation modeling module, and a maximum a posteriori probability position of the abnormal event is determined by using a Bayesian theorem framework; finally, an optimal isolation strategy and a recovery operation sequence are determined in combination with valve facility position information. The application can accurately restore an abnormality propagation path, improve positioning accuracy, and maximize the reduction of gas supply interruption range, so as to realize safe and refined management of the gas pipe network.

The invention relates to the technical field of data prediction, in particular to a tumble risk detection method based on multi-modal health data. For the feature data of the user, tumble risk prediction is carried out through an XGboost method, and a first tumble risk probability corresponding to the current moment is obtained; for the feature data of the user, the prediction accuracy of the large model is improved through an SFT fine tuning mode, and a second fall risk probability corresponding to the current moment is obtained; fusing the first tumble risk probability and the second tumble risk probability to obtain a comprehensive tumble risk probability; and taking the comprehensive fall risk probability as a prior probability in the Bayesian theorem, and obtaining a fall prediction probability through the Bayesian theorem. According to the method, multi-modal fusion and dynamic Bayesian updating are realized, the sign time sequence and the static health information are subjected to fusion analysis for the first time, the model robustness is improved, individualized risk tracking is realized, and the method adapts to health changes.

Feedforward control uses a Gaussian process representation of system input to derive an optimum input for a desired output trajectory over discrete sample times. A model provides a forward transfer function. A kernel function, having selectable parameters, provides a prior covariance matrix of the Gaussian process. A posterior mean of the system input can be derived by Bayes' theorem, based on the model, the kernel function, and the desired output trajectory. The posterior mean predicts system output and, thereby, tracking error. The kernel function parameters are selected to optimize the tracking error. The corresponding posterior mean provides optimized system input for the desired output trajectory. Disclosed techniques are applicable even when model inversion is unstable. Disclosed techniques can be applied in segments, saving computation resources and also suitable for adaptive control.

The invention discloses a malodorous gas traceability method, and belongs to the traceability technology of pollutants. Aiming at the characteristics of short emission time, fast diffusion, difficult capture and the like of odor pollution, the method comprises the following steps: firstly, constructing a high-resolution meteorological field, then obtaining a pollutant possibility distribution diagram by combining a pollution wind rose diagram and a Bayesian theorem, and constructing an odor pollution source fingerprint spectrum on the basis of an improved Lagrange trajectory algorithm and an odor pollution source fingerprint spectrum construction technology. And a foul smell source is obtained from the possibility distribution diagram of the pollutants, so that rapid and accurate source tracing of the foul smell is realized. According to the method, multiple technical innovations are integrated, so that low-cost, quick and accurate positioning of the odor pollution source is realized. The traceability efficiency and accuracy can be greatly improved, and a scientific basis is provided for accurate management and control and targeted treatment of environmental pollution events.

The application discloses a kind of variable air volume air conditioning system sensor online calibration methods based on physical model, comprising the following steps: S1, define variable air volume air conditioning system sensor multiple groups steady-state measurement value;S2, for variable air volume air conditioning system, according to energy conservation and pressure conservation these physical models and the steady-state measurement value obtained in S1 are modeled respectively to system sensor;S3, using Bayes theorem and Monte Carlo random sampling is calibrated to temperature, humidity, flow and differential pressure sensor in different calibration domain of variable air volume air conditioning system.The application combines historical data and the physical model of system, on the basis that new sensor is not removed or installed, for the sensor offset of different working environment and aging, after calibration, the accuracy of sensor measurement value has greatly promoted, guarantee the accuracy and stability of sensor bottom layer data, so that variable air volume air conditioning system more reliably operates.

The invention discloses a method for predicting the residual life probability of a voltage regulating device by fusing a physical mechanism and machine learning, and belongs to the technical field of voltage regulating device fault prediction and health management. The method aims at solving the problems that parameters of an existing service life prediction model of the fuel gas pressure regulating device are fixed, and the state change response capacity is poor. Comprising the steps of collecting principle data, obtaining feature data and splicing the feature data into an initial feature matrix; calculating the weight to obtain a screened feature matrix; performing principal component analysis to obtain a low-dimensional feature vector; eigenvalue decomposition is carried out, weighted fusion is carried out on main component scores, and a comprehensive health index is constructed; the low-dimensional feature vectors serve as training data to train a model, and an XGBoost-Cox model is obtained for life distribution prediction; calculating by adopting a physical mechanism model to obtain physical prior life distribution; and fusing the data-driven life distribution with the physical prior life distribution by adopting the Bayesian theorem to obtain the fused posterior life distribution of the voltage regulating device. The method is used for predicting the residual life of the pressure regulating device.

The invention discloses an athlete action performance analysis method based on big data analysis, and particularly relates to the technical field of action performance, and the method comprises the steps: monitoring and determining a penalty process of an athlete through a sensor, and analyzing the optimal track of each shooting action through clustering according to a time sequence of extracting key feature points of a forearm, and calculating a minimum cost path between the current penalty ball track and the optimal track through a dynamic time warping algorithm, calculating a success probability under the current forearm angle amplitude through a Bayesian theorem based on the forearm angle amplitude of each penalty ball of the athlete, and determining personalized expression information of the forearm action of the athlete. By collecting the speed time sequences of different feature points of the forearm of the athlete in the penalty process and fitting the relationship between the speed and the time by using the least square method, the actual performance information of the forearm action of the athlete is determined, and the method is beneficial to the personalized analysis of the influence of the forearm on the penalty hitting in the penalty process of the athlete.

The invention discloses an unmanned aerial vehicle communication fault diagnosis method based on Bayesian decision and man-machine cooperation, and belongs to the technical field of unmanned aerial vehicle fault prediction and health management. The method comprises the following steps: firstly, acquiring three types of multivariate operation parameters including a link state, a hardware parameter and a flight state through a real-time acquisition module, and preprocessing by adopting a multi-mode adaptive filtering and parameter discretization unit; secondly, a fault knowledge base is constructed, the prior probability and the likelihood probability are calculated based on the labeled samples, Gaussian kernel density estimation is adopted for continuous parameters, and polynomial distribution modeling is adopted for discrete parameters; further, constructing a maximum posterior probability decision engine based on the Bayesian theorem, and outputting a fault diagnosis result and a confidence level; finally, through a human-in-the-loop mechanism, expert intervention is started when the confidence coefficient is insufficient or manual rechecking errors exist, and the probability model is dynamically updated by utilizing a correction sample, so that system self-optimization is realized. According to the method, the three problems of insufficient multi-source parameter coupling analysis, poor static rule base adaptability and low manual intervention efficiency are effectively solved, and the fault diagnosis accuracy and the system adaptive capacity in a complex environment are remarkably improved.