188 results about "Dynamic Bayesian network" patented technology

The embodiment of the invention provides a modeling method of an early warning model of mixed failures and a modeling system. The modeling method provided by the invention comprises the following steps of: generating a function analyzing module on the basis of HAZOP (Hazard and Operability Analysis) or FMEA (Failure Mode and Effects Analysis); generating a degeneration analyzing module on the basis of FMEA analyzing results and a theory of stochastic processes; generating an accident analyzing module according to state monitoring data and maintenance action information; generating an action analyzing module according to output results of the function analyzing module and the degeneration analyzing module through combining a DBN (Dynamic Bayesian Network) theory; taking the output of the accident analyzing module as an inference evidence and utilizing a DBN inference algorithm to process forward and backward inferences in the same time period to generate an evaluating module for outputting factors and consequences of system failures; taking the output results of the evaluating module and the accident analyzing module as the inference evidence and utilizing the DBN inference algorithm to process forward and backward inferences in the different time periods to generate a predicating module for outputting prospective degeneration tendencies of each member of the system. The model provided by the invention can be used for tracking the failure factors of the system and inferring possible failure consequences and probability.

The present disclosure belongs to the field of offshore oil, and in particular relates to a method for assessing the safety integrity level of offshore oil well control equipment. The method for assessing the safety integrity level of the offshore oil well control equipment comprises three major steps: creating a safety instrumented function evaluation module and dividing the related devices for performing the safety instrumented functions into a sensor subsystem; a controller subsystem and an actuator subsystem, establishing a dynamic Bayesian network model for respective subsystems for calculation; and integrating, analyzing and optimizing the safety integrity data of the subsystems.

A system and method for classification of passenger vehicles and measuring their properties, and more particularly to a stochastic multi-class vehicle classification system, which classifies a vehicle (given its direct rear-side view) into one of four classes Sedan, Pickup truck, SUV / Minivan, and unknown, and wherein a feature pool of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector, and the feature vector is then processed by a Hybrid Dynamic Bayesian Network (HDBN) to classify each vehicle.

Performing trio-based phasing includes: obtaining a set of preliminary phased haplotype data of an individual; establishing a dynamic Bayesian network based at least in part on the set of preliminary phased haplotype data of the individual and phased haplotype data of at least one parent of the individual; and determining, based on the dynamic Bayesian network, a set of refined haplotype data of the individual.

The invention discloses a state threat assessment method based on a fuzzy dynamic Bayesian network. The method includes extracting data to be processed from acquired data; processing the acquired data in a jamming proofing manner, and processing the data processed in the jamming proofing manner in a fuzzy manner; performing knowledge representation on the process of battlefield state threat assessment, determining concept and expression of events and attributes, and establishing a dynamic Bayesian network model about battlefield threat assessment; inputting fuzzy variables into the network model to obtain the fuzzy dynamic Bayesian network, performing inference on the fuzzy dynamic Bayesian network to acquire a state forecasting result, and acquiring the threat assessment grade based on the state forecasting result. According to the method, the dynamic Bayesian network is established, knowledge representation is performed on data changing with the time, timeliness and effectiveness of the data are enhanced, the data processed in a fuzzy manner are inputted as inputting evidences, accuracy and efficiency of inference are improved, and a semantic model of the state threat assessment can be expressed better.

One or more embodiments of techniques or systems for generating turn predictions or predictions are provided herein. Environment layout information of an operating environment through which a first vehicle is travelling may be received. A current location of the first vehicle may be received. One or more other vehicles may be detected. Additional environment layout information from other vehicles may be received. A model including the operating environment, the first vehicle, and one or more of the other vehicles may be built. The model may be based on the environment layout information and the additional environment layout information and indicative of an intent of a driver of one of the other vehicles. Further, predictions may be generated based on the model, which may be based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), a Dynamic Bayesian Network (DBN), or a combination thereof.

A system and method for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and / or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.

The invention provides a fault diagnosis and pre-warning system in the oil refining production process and an establishment method of the fault diagnosis and pre-warning system. The method comprises the steps of establishing a multilevel flow model in the oil refining production process, determining a fault propagation path, conducting hazard and operability (HAZOP) analysis on the oil refining production process, determining a plurality of static nodes and a plurality of dynamic nodes, determining the connected relation of the static nodes and the dynamic nodes, establishing at least one alternative model, adopting the dynamic Bayesian network structure scoring mechanism for scoring of the alternative models, determining the alternative model with the highest score as a fault multilevel related model, determining the conditional probability relation between the static nodes and the dynamic nodes in the fault multilevel related model, and establishing a fault diagnosis and pre-warning module. The established fault multilevel related model and the fault diagnosis and pre-warning module have the higher fault propagation and fault tracing and reasoning capability, and accurate diagnosis and timely warning of faults generated in the oil refining production process can be achieved.

The invention belongs to the technical field of automatic vehicle driving environment cognition and decision-making, and especially relates to a vehicle trajectory predicting method based on hybrid dynamic bayesian networks and gaussian process. According to the method, parameters of MDBN and GP are learned through natural vehicle driving data, and a plurality of vehicle kinematic models are combined through utilizing MDBN, so that short-term trajectory prediction and estimated probabilities of driving intention and driving characteristics are obtained, and then long-term trajectory predictionand representation of uncertainty prediction are conducted through using GP. By adopting the method, short-term prediction characteristics based on a vehicle physical movement model as well as long-term trajectory prediction and representation of uncertainty prediction according to driver information can both taken into account. Compared to an existing vehicle trajectory predicting method, vehicle models, abstract intention and data driving are combined together, and the expansibility of the MDBN model and the GP model are strong, and thus the method is suitable for different driving scenarios and can combine more effective situational information like road information and traffic information.

The invention discloses an intersection collision-avoiding method based on a dynamic Bayes network, and mainly solves the problems that an existing algorithm is not well adapted to the complex road layout of an intersection, a large amount of data processing is needed, and the calculation complexity is high and the time complexity is high. The method comprises the steps of: 1) determining vehicle states, road conditions and driver behavior information, and adopting the dynamic Bayes network to carry out modeling on vehicle state evolution; 2) determining the safe driving behavior of a target vehicle according to the current environment conditions; and 3) deriving the intention behavior of the driver at the intersection, carrying out risk estimation based on comparison between the safe driving behavior and the intention behavior, and when potential risks are detected, taking different measures according to the practical condition for avoiding a collision. According to the invention, the complex vehicle track prediction process is avoided, the calculation amount is reduced, the vehicle collision in other scenes can be flexibly avoided, and the method can be applied to an intelligent traffic system.

The invention relates to a turn predictions. One or more embodiments of techniques or systems for generating turn predictions or predictions are provided herein. Environment layout information of an operating environment through which a first vehicle is travelling may be received. A current location of the first vehicle may be received. One or more other vehicles may be detected. Additional environment layout information from other vehicles may be received. A model including the operating environment, the first vehicle, and one or more of the other vehicles may be built. The model may be based on the environment layout information and the additional environment layout information and indicative of an intent of a driver of one of the other vehicles. Further, predictions may be generated based on the model, which may be based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), a Dynamic Bayesian Network (DBN), or a combination thereof.

The invention relates to video processing, mode identification and the like. In order to provide a video abnormal event detection method by which a multi-motion parameter variation rule of the same event in a plurality of visual angles of a cross camera can be represented and of which the accuracy is high, the technical scheme of the invention is that: a method for detecting a monitored video abnormal event based on trace analysis comprises the following steps of: 1, classifying abnormal events based on the trace analysis; and 2, modeling the abnormal events based on a dynamic Bayesian network, wherein the modeling is finished by the following three steps of: a, constructing the dynamic Bayesian network which consists of three levels from bottom to top, namely a characteristic layer, an element layer and an event layer; b, learning the dynamic Bayesian network in the following three situations: firstly, the labels and the number of elements are known; secondly, the number of the elements is known and the labels are unknown; and thirdly, the number of the elements and the labels are unknown; and c, selecting a characteristic. The method is mainly applied to video processing, mode identification and the like.

The invention discloses a method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm is used for carrying out static state planning of route and real-time dynamic re-planning of route when an unmanned plane executes a single task. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm includes the steps: constructing a digital map through a landform model and a simplified threat model, considering the influence of space division granularity on the complexity of an optimizing control algorithm, and realizing division of space according to a fence self-adaptive algorithm; realizing static state route planning by means of an improved artificial fish swarm algorithm; and considering the time factor, constructing a threat prediction model based on a dynamic Bayesian network, predicting the unexpected threat, combined with flight constraint of the unmanned plane, obtaining the re-planning starting point, and realizing global route dynamic re-planning by means of the improved artificial fish swarm algorithm. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm has the advantages of reducing the complexity of a route optimizing control algorithm, improving the optimum route searching capability, and satisfying the practical route planning demand.

The invention belongs to the technical field of road traffic detection and particularly discloses an expressway road traffic state estimation method based on a dynamic Bayesian network; the method comprises the following steps: (1) extracting relevant parameters of the road traffic state as nodes; (2) determining an interrelationship among the nodes and establishing the dynamic Bayesian network; (3) carrying out a fuzzy classification on data of the observable nodes, analyzing the historical data to obtain a clustering center of each classification and determining a membership degree of the data of the observable data, belonging to each classification; 4) for a target node selected in the dynamic Bayesian network, acquiring a corresponding conditional probability and a transition probability and establishing each moment characteristic table of the selected target node; 5) inputting road traffic flow parameters of the current moment to the dynamic Bayesian network and triggering to reason a target of each moment to obtain a traffic state estimation result. According to the expressway road traffic state estimation method disclosed by the invention, the uncertainty in a single parameter estimation state is solved and simultaneously the relevance in the traffic state is considered, so that better effect and reliability when the road traffic state is estimated are achieved.

The invention relates to an ecological sound identification method on the basis of rapid sparse decomposition and deep learning, which is characterized by comprising the following steps: S01, respectively carrying out OMP (Orthogonal Matching Pursuit) sparse decomposition on pure sound and test tape noise and correspondingly outputting reconstruction signals and OMP characteristics of the pure sound and the test tape noise; S02, respectively extracting composite characteristics comprising the OMP characteristics from the pure sound and the test tape noise; S03, carrying out DBN (Dynamic Bayesian Network) model training on the composite characteristics extracted from the reconstructed pure sound; and S04, carrying out DBN model classification on the composite characteristics extracted from the reconstructed test tape noise and the trained pure sound and outputting an ecological sound category which the test tape noise belongs to. The ecological sound identification method has a more obvious effect of improving noise resistance and robustness of a system.

The invention discloses a meteorological threat assessment method based on a discrete dynamic Bayesian network. The method comprises the following steps: collecting an observed weather type, intensity information and UAV (Unmanned Aerial Vehicle) position and attitude information; performing a quantization treatment according to a divided quantization level, and establishing an observation evidence list; using expert knowledge or experience to establish a conditional probability transfer matrix between states, and determining a state transfer matrix between time slices; establishing a discrete dynamic Bayesian network model between a meteorological threat level, a meteorological factor and the UAV; and using a Hidden Markov Model reasoning algorithm to calculate the final meteorological threat level. The meteorological threat assessment method based on the discrete dynamic Bayesian network provided by the invention realizes the organic combination of a continuous observation value and the discrete dynamic Bayesian network, and reasons out the probability distribution of a meteorological threat degree in combination with the HMM (Hidden Markov Model) reasoning algorithm, so that the effectiveness, the practicability and the accuracy of meteorological assessment can be greatly improved.

The invention discloses a method for estimating parameters of reliability models of multi-state systems. The method comprises the following steps: collecting state information of a monitored multi-state system in the usage phase; establishing a dynamic Bayes network reliability model of the system according to composition logic of units in the monitored multi-state system so as to update state information of all nodes in a Bayes network and obtain posterior state probability distribution sequences of all the units; and estimating transfer parameters of the units by utilizing the posterior state probability distribution sequences of the units in the system. According to the method provided by the invention, the Bayes network is used for carrying out state information reasoning on all the units in the multi-state system; the observation information can come from each layer of the system; the observation can be complete information or incomplete information, so that the information transfer among different layers of the system can be realized and information reasoning under the condition of incomplete state information can be realized so as to improve the correctness of parameter estimation.

The invention belongs to the field of traffic safety and intelligent traffic management control, in particular to a real-time estimating method for high-grade road traffic flow running risks. The real-time estimating method for the high-grade road traffic flow running risks considers the problem that a high-grade road short of fixed-point traffic flow collecting facility is incapable of acquiring the road traffic flow rate, occupancy and the like traffic parameters. The real-time estimating method for high-grade road traffic flow running risks includes that using traffic flow speed data acquired through different traffic information collecting technologies to build a real-time accident forecasting model, using a dynamic Bayesian network model to consider the speed state data of several time periods, building relationships between the traffic flow state and accident risks, and estimating the accident in real time so as to early warn or regulate a vehicle to avoid an accident. The real-time estimating method for the high-grade road traffic flow running risks has good forecasting precision for the high-grade road accident risks through speed data, and the real-time estimating method for the high-grade road traffic flow running risks has broad practical application value.

The invention discloses a mechanical system time varying reliability evaluating method based on a dynamic Bayesian network. The mechanical system time varying reliability evaluating method comprises a first stage of determining model basic indexes, a second stage of structuring the structure of the Bayesian network and a third stage of updating a formula and the time varying reliability of a Monte Carlo simulation computer mechanical system according to Bayesian information. The mechanical system time varying reliability evaluating method has the advantages that a knowledge diagrammatic expression method is provided through the Bayesian network, directed diagrammatic expression can be carried out on the cause and effect probability relation between node variables, and the cause and effect probability relation can be used for uncertain knowledge expression, cause and effect reasoning, diagnosis reasoning and the like. The weak link of the reliability of the system can be effectively recognized through reasoning of the Bayesian network; the relation between components in the mechanical system becomes more visual and clear through diagrammatic display, the dynamic Bayesian network technology is applied to evaluation of the time varying reliability of the mechanical system, the multiple states and failure correlation of the mechanical system are analyzed, and a theoretical support is provided for improving the performance and the reliability of the mechanical system.

The invention relates to an equipment state identification method based on a Markov model and a probability network. The state of equipment is identified using a hierarchical hidden Markov model (HHMM), and the identification result can be calculated more accurately in the form of probability. In view of the problem that the model parameters exponentially increase with the increase of equipment states, a dynamic Bayesian network is introduced to reduce the computational complexity of the model and shorten the inference time. HHMM is expressed as a dynamic Bayesian network. The health state of equipment is identified with the help of a preprocessed vibration signal. In view of the limitation of the existing state classification method, a state number optimization method based on a K-means algorithm and a cross verification method is presented. Through the state number optimization method, the stages in the process of fault development can be divided more accurately to lay a foundation for the accurate identification of equipment states. The change of the health state can be detected before functional failure, and the remaining life of equipment can be predicted using a trained model according to the change observed in the current behavior.

The invention provides a bayesian-network-based sensor fault diagnosis method in a complex system. A sensor monitoring model is constructed in a complex system; and a dynamic Bayesian network model is applied to a sensor fault diagnosis of the complex system. To be specific, the method comprises the following steps: step one, establishing a single sensor dynamic model S<(I,q)> of a complex system according to all sensor characteristics; step two, establishing a Bayesian network model for the complex system; step three, selecting a non-linear observer sigma <(I,q)>, constructing a sub system estimation model, and obtaining a sensor residual error; step four, estimating a residual error threshold value and determining prior probability distribution of observation node parameters; step five, carrying out updating and adjusting on a Bayesian network structure and parameters and constructing a new Bayesian model; and step six, realizing the sensor fault diagnosis method in the complex system based on the Bayesian network. According to the method, expansion is carried out based on the Bayesian network; and for the complex system, the method has obvious advantages of the complex system fault diagnosis on the condition of information incompleteness.

A computer-implemented method for speech recognition, comprising the steps of: registering (201) by means of an input device (102A), electrical signal representing speech and converting the signal to frequency or time-frequency domain (202), analyzing the signal in an analysis module based on Dynamic Bayesian Network (205) configured to generate hypotheses of words (W) and their probabilities on the basis of observed signal features (OA, OV), recognizing (209) a text corresponding to the electrical signal representing speech on the basis of certain word (W) hypotheses and their probabilities. The method is characterized by inputting to the analysis module (205), observed signal features (308-312), which are determined for the signal in frequency or time-frequency domain (202) in at least two parallel signal processing lines (204a, 204b, 204c, 204d, 201a) for time segments distinct for each line, and analyzing in the analysis module (205) relations between observed signal features (308-312) for at least two distinct time segments in the analysis module (205).

The invention provides an intelligent immersive high-speed train virtual driving behavior evaluation method and a simulation system. According to the method, a virtual driving scene module is established, and various driving training scenes composed of multiple intelligent agents are constructed; a high-speed train dynamics module is established, including various subsystems of the high-speed train such as engine, power train system, steering mechanism and body modules; a virtual driving scene simulation module is established; a high-speed train logic simulation module is established; a data helmet display and man-machine interaction module is established, the head tracking technology of a helmet type display is researched on the basis of the virtual reality technology, and motion of the high-speed train in a virtual environment is controlled and manipulated through a high-speed operation mechanism; finally, intelligent driving behavior researching and evaluation are realized on the basis of a time varying dynamic bayesian networks (TVDBN) dynamic learning learning model according to the virtual driving operation data accumulated in a user and simulation system interaction process.

The invention discloses a method for analyzing reliability of a turbine blade disk system of an aircraft engine. The method comprises the following steps of: establishing a system chart; establishing a system dynamic bayesian network and a system failure-mode dynamic bayesian network model; discretizing and transforming the dynamic bayesian network into multiple static bayesian networks; structurally decomposing the static bayesian networks into simply-connected regional networks and multiply-connected regional networks; performing bidirectional derivation on the simply-connected regional networks by a static bayesian network inference method; and performing bidirectional derivation on the multiply-connected regional networks by a bucket elimination method to respectively solve the failure rate of the turbine blade disk system, namely a system element, of the aircraft engine, and the fault rate of each failure mode. According to the method for analyzing the reliability of the turbine blade disk system of the aircraft engine, the problems of complicated expression, low computational efficiency, combinatorial explosion and the like, which are caused when traditional reliability analyzing methods are used for analyzing large-scaled, complicated and dynamic structures, are solved, and the computational efficiency is enhanced.

The invention discloses a common cause failure system reliability evaluation method considering environmental factors. The invention fully takes into account the common cause failure and failure ratevariation of the modern complex system due to the characteristics of the multi-task section, the complex environment and the multi-components. Therefore, the present invention has more engineering value than the traditional system reliability evaluation method which assumes that the component failure is independent and is not affected by the environment. At the same time, the invention uses the dynamic Bayesian network modeling method, which can reasoning the complex system, so that the method is applicable to the reliability evaluation of the large-scale complex system.

The invention belongs to the field of petroleum engineering, and particularly relates to a digital twin-driven offshore oil underwater production system fault prediction method and system. The digitaltwin-driven offshore oil underwater production system fault prediction method comprises five big steps: determining degradation modes of modules, modeling a performance degradation process dynamic Bayesian network, calculating dynamic reliability, calculating residual life and managing and updating life information based on digital twinning. A digital twin-driven fault prediction system for an offshore oil underwater production system comprises an overwater control module data acquisition and processing subsystem, a hydraulic power unit data acquisition and processing subsystem, an electric power unit data acquisition and processing subsystem, an underwater control module data acquisition and processing subsystem, an underwater Christmas tree data acquisition and processing subsystem anda digital twin life monitoring control system.

The invention discloses a threat estimation method based on a dynamic Bayes network, relating to the technical field of situation estimation. The method is realized by the following steps of 1. performing data collection and arrangement and extracting the situation elements; 2. integrating various situation elements, and establishing dynamic bayesian network topology; 3. performing network parameter learning and setting; 4. realizing fast approximation Bayesian inference based on Markov property; 5. combining each threat level probability into a continuous threat indexes and a discrete threat level; and 6. outputting the treat estimation result. The method can integrate the various situation elements, and perform reasonable and intelligent inference analysis, thereby realizing the blue group object threat dynamic quantitative and qualitative estimation and being applicable to a situation estimation and commanding control system.

The invention relates to a method for estimating the health status of a lithium battery on the basis of a dynamic Bayesian network. The current method cannot meet the requirement of online detection and is poor in precision. A large amount of training data is acquired through ageing tests, and then a corresponding dynamic Bayesian network model is trained for a plurality of battery health statuses. In a real-time estimation stage, the voltage data of the battery is acquired for one time at set intervals, and the dynamic Bayesian network model to which a voltage sequence belongs is calculated through forward procedure recursion, so that the health status of the battery is accurately estimated. The health status of the battery can be conveniently estimated in real time by the method, the calculation speed is high, and the estimation is accurate.

The invention discloses a method for polymorphic and dynamic reliability assessment for a shield cutterhead and a drive system. The method comprises the following steps of firstly, establishing a dynamic fault trees for failure of an earth pressure balance shield machine cutterhead and a drive system; secondly, determining a failure rule for a polymorphic and dynamic gate and extending an established dynamic fault tree model to polymorphism; and lastly, establishing a dynamic Bayesian network model for system failure for a dynamic Bayesian network transformation rule according to the dynamic fault tree and determining system reliability. According to the method, a problem that the dynamic reliability analysis of the shield cutterhead and the drive system cannot consider polymorphism, dynamics and interdependency simultaneously is solved, the dynamic reasoning can be carried out according to the statistical data for failure of basic events, and the more efficient and accurate decision information is provided for fault maintenance and repair of a shield machine.

Application of a dynamic Bayesian network to intelligent diagnosis of a mechanical equipment failure belongs to the field of failure diagnosis. According to the application, the dynamic three-layer Bayesian network is applied to intelligent diagnosis of the mechanical equipment failure for the first time; and a possible failure and the failure probability of related mechanical equipment can be calculated and deduced by acquiring operating state information of the mechanical equipment and setting and controlling related relation among Bayesian network nodes and the probability. The method has the advantages of real-time property, continuity, high accurate rate and the like, and is suitable for failure diagnosis of various kinds of mechanical equipment.