33 results about "Dynamic Bayesian network" patented technology

This invention provides a method and system for train operation risk assessment based on dynamic Bayesian networks. The method includes: acquiring multi-source heterogeneous data involved in train operation; modeling the multi-source heterogeneous data using a dynamic Bayesian network to construct a causal relationship graph containing time slices, generating an initial risk assessment model; updating the node variables in the initial risk assessment model in real time to obtain a target risk assessment model; calculating the probability and severity of each risk factor using a conditional probability inference algorithm based on the target risk assessment model; generating risk response strategies using a decision tree algorithm based on the probability and severity of each risk factor; and introducing a distributed monitoring mechanism to continuously track the train operation status and record feedback data for optimizing the risk assessment model during the execution of the risk response strategies. This invention significantly improves the accuracy, timeliness, and adaptability of train risk assessment.

This application relates to a risk assessment method, apparatus, device, storage medium, and program product for secondary equipment. Based on the fluctuations and trends of alarm frequency sequences associated with the inherent risk factors of the target equipment, the risk index of the inherent risk factors of the target equipment at the current moment is calculated. Based on the risk index of the target factors, the dynamic conditional probability of the target factors is determined using a logistic regression model. The target factors are inherent risk factors with parent nodes in a dynamic Bayesian network. The dynamic conditional probability is input into the dynamic Bayesian network, and the systemic risk probability of the target factors is determined through a Bayesian inference algorithm. Based on the systemic risk probability of the target factors, their operational impact factors, economic impact factors, and safety impact factors, the risk assessment result of the secondary equipment is determined using a comprehensive risk index expression. This improves the accuracy of the obtained risk assessment results for secondary equipment.

The application discloses a unified probability propagation prediction method and system suitable for WUI fire, and the method comprises the following steps: constructing a unified state space and discretizing; constructing an external driving model of building layer fire propagation; constructing a building layer dynamic Bayesian network with each building as a node, parameterizing a conditional probability table of the building layer dynamic Bayesian network based on a physical driving mechanism; establishing a mapping relationship between an internal state of the building layer and an external observation category; realizing state correction and rolling prediction based on Bayesian updating; when external observation is obtained, the system state is updated based on Bayesian updating in combination with observation information, a posterior state distribution is obtained, the posterior state distribution is taken as an initial state of the next time step prediction, a rolling assimilation closed loop is formed, and finally a multi-state probability risk field of building scale evolution over time is output. The application can realize unified coupling of a landscape, a building and a flying fire, obtain state probability output over time, and can be assimilated and corrected.

This invention discloses a method for tracing the source of construction power supply accidents based on causal inference, comprising the following steps: collecting data on current, voltage, switch status, and equipment operation events at the construction site, and performing time synchronization processing to generate a construction power supply state sequence; dividing the state sequence into time slices according to preset intervals to form a time window data set; constructing a dynamic Bayesian network model based on the time window data, establishing time slice nodes and conditional probability connections; assigning conditional probabilities to nodes and determining accident constraint nodes; performing reverse probabilistic inference starting from the accident constraint nodes, calculating the conditional probabilities of upstream nodes and generating a propagation path set; sorting the path probabilities, determining the target tracing path, selecting the node with the highest probability in the path as the root cause node, and outputting the root cause node and its corresponding path. This invention achieves causal source tracing analysis of construction power supply accidents through a time-sliced ​​dynamic Bayesian network and reverse probabilistic inference.

A two-stage distributed process running state evaluation method based on spatio-temporal correlation information guidance collects offline time series data for preprocessing and sliding window division, decomposes the data using the DTW algorithm, divides the sub-blocks and reconstructs the initial spatio-temporal correlation topology; through dynamic spatial attention refining the topology and constructing a dynamic adjacency matrix, combining GCN to extract sub-block spatial features and Transformer to extract time series features, a sub-block level spatio-temporal feature learning module is established through spatio-temporal fusion and auxiliary supervision training; through the sub-block feature adaptive layer to align heterogeneous features, use multi-head attention to construct a process level adjacency matrix, combine process level GCN and Transformer to extract interaction and evolution features, after dimension reduction and classifier output prediction probability, combine double loss and Adam algorithm to fine-tune parameters to get an offline evaluation model; collect real-time data to infer the performance level, under non-optimal state, filter abnormal variables through attention matrix, and locate root cause variables with the help of dynamic Bayesian network. This method can realize efficient and accurate root cause diagnosis.

The application relates to a deep space multi-disaster superimposed instability evaluation and advanced control system and method, and belongs to the technical field of underground engineering safety and intelligent operation and maintenance. The system comprises a multi-source synchronous monitoring module, an edge computing and data fusion module, a disaster evolution dynamic evaluation and prediction module and an advanced control execution module which are sequentially connected. The multi-source synchronous monitoring module comprises a vibration sensor array, a seepage pressure sensor array, a strain sensor array and a corrosion sensor array. The method comprises the following steps: installing a multi-source synchronous monitoring module; collecting and fusing multi-source heterogeneous data; constructing a dynamic Bayesian network model, recursively calculating the prior and posterior probabilities of disaster nodes; calculating the path trigger probability and evaluating the consequence severity, screening out the key path with high trigger probability and serious consequences; locking the intervention target node according to the key path identification result, establishing a multi-objective optimization model, and quantitatively generating a targeted quantitative intervention strategy; implementing precise intervention measures and real-time monitoring of response data. The application can greatly improve the initiative and reliability of deep engineering safety operation and maintenance.

The application discloses a data processing method and device for Maotai-flavor base liquor, electronic equipment and medium, and relates to the technical field of data security. The method comprises the following steps: acquiring a triple data subjected to a digital signature and an encryption operation, wherein the triple data is obtained by processing real-time data collected by a sensor array by a sensor control unit, the sensor array is arranged around a base liquor jar body of a depositary, and is connected to the sensor control unit; decrypting and checking the triple data, and after the checking is passed, calling a dynamic Bayesian network probability parameter corresponding to the current base liquor in a pre-constructed feature library of a cloud trusted data space, and inferring a state type of the real-time data; performing zero-knowledge proof verification on the state type, grading early warning according to the verification result, and writing a hash value of an early warning log into a customer interest block of a block chain. By using the method, the state of the base liquor can be monitored in real time, and the credibility is improved.

This invention discloses a method and system for collaborative control of a drone swarm, relating to the field of drone control technology. The method includes defining roles within the drone swarm; a coordinator drone receiving a task request from a task initiator; the coordinator drone allocating tasks; collaborating drones verifying the task allocation information upon receipt; after successful verification, observer drones monitoring the operational status of each collaborating drone node in real time to obtain monitoring data; each collaborating drone, based on the monitoring data, using a sliding window-based dynamic Bayesian network to evaluate the status of other collaborating drones in real time, generating a health score for the corresponding node and dynamically adjusting consensus-reaching conditions based on the health score; executing the task when a collaborating drone determines that consensus has been reached; and after the task is completed, the coordinator drone, upon receiving task feedback information, reporting the task completion status to the task initiator.

The application provides a chronic disease intelligent management method and device based on multi-source data fusion, which decomposes overall state vector data of a patient according to a time scale, constructs a three-body coupled singular perturbation equation set, carries out fast manifold dynamics derivation on the three-body coupled singular perturbation equation set, and constructs a medical subsystem; wherein the medical subsystem comprises a plurality of adjustment models corresponding to personal physiological index data; the three-body coupled singular perturbation equation set is subjected to medium manifold dynamics derivation, a rehabilitation subsystem is constructed based on a Markov decision process; the three-body coupled singular perturbation equation set is subjected to slow manifold dynamics derivation, and a maintenance subsystem is constructed based on a dynamic Bayesian network; the medical subsystem, the rehabilitation subsystem and the maintenance subsystem are used to construct a chronic disease intelligent management model; current medical data, rehabilitation data and maintenance data of a patient are input into the chronic disease intelligent management model to obtain a chronic disease intelligent management scheme, thereby solving the problem of time sequence phase conflict in the prior art.

The application discloses a marine environment multi-parameter coupling equipment fault diagnosis method, and belongs to the technical field of marine equipment state monitoring and fault diagnosis. Real-time observation values of multiple environment parameters are collected, a multi-parameter coupling strength index is calculated, a forbidden edge set and a necessary edge set are constructed, a hard constraint is imposed on data-driven causal diagram learning, a directed acyclic graph is output, a hierarchical dynamic Bayesian network including an environment layer, a degradation layer and a fault layer is constructed, and the multi-parameter coupling strength index is introduced into the state transition probability of the degradation layer to realize dynamic modulation; a multi-fault concurrent scene is decoupled and identified, independent concurrent faults and composite faults are distinguished, and component confidence is output, and online continuous learning is realized through a Bayesian conjugate increment method. The application effectively solves key problems such as multi-environment parameter coupling effect quantization, fusion of physical constraints and data driving, and sensor signal quality self-adaptation, and has long-term self-adaptation capability to service environment changes and new fault types.

The application relates to the technical field of probabilistic graphical models, in particular to a power consumption portrait construction method and system based on a dynamic Bayesian network. Multiple hidden variables are introduced to describe the preference degree of a user for various power consumption attributes, and a power consumption portrait based on a DBN is constructed to intuitively and reasonably explain the influencing factors of power consumption rules; a VQVAE model and a GPN model are introduced to reduce the power consumption preference hidden variables, the reduction does not change the original power consumption rule distribution, the power consumption portrait accurately describes the dynamic relationship between the variables, the construction cost of the power consumption portrait is reduced, the power consumption portrait template is constructed to uniformly learn the relatively fixed relationship within and between time slices, and the related local structure in the template is updated in combination with the power consumption data of adjacent time slices, so that the dynamic relationship existing within and between time slices is captured, and efficient construction of the power consumption portrait is realized. The application aims to solve the problem of how to optimize the construction of the power consumption portrait.

This invention belongs to the field of mining engineering and addresses the problems of large data transmission volumes, incomplete risk assessments, and delayed early warnings in traditional mine safety management systems. It proposes a new mine safety management system. The data acquisition module collects multi-dimensional raw data, including mine environmental parameters and geomechanical data, through sensors. The edge computing module deploys nodes in the acquisition area to locally clean, denoise, and extract features from the raw data, generating secondary data which is then transmitted via the information transmission module. The data processing module incorporates a risk emergence prediction component using multimodal fusion and dynamic Bayesian networks. It integrates multimodal features and dynamically simulates risk evolution to calculate risk levels. The data response module pushes early warning information to personnel at different levels through multiple channels based on the risk level. This system reduces data transmission volume, improves the comprehensiveness of risk assessment, and enables proactive risk prediction, meeting the needs of real-time mine safety monitoring and efficient management.

This invention discloses an intelligent method for mining the root causes of abnormal production yield in probe devices, belonging to the field of industrial intelligent analysis technology. The method includes the following steps: acquiring probe final inspection data and historical yield anomaly case data; preprocessing the probe final inspection data and comparing it with a preset threshold to obtain final inspection failure modes; performing correlation mining on historical yield anomaly case data based on the final inspection failure modes to obtain a set of associated sensitive processes; acquiring process production time-series data and constructing process time-series dependency chains; filtering nodes and removing branches in the process time-series dependency chains to obtain process transmission compression paths; using an improved Granger causality test to determine the abnormal transmission of the process transmission compression paths to obtain a set of substantial abnormal nodes; acquiring time-series slice data, inputting it and the set of substantial abnormal nodes into an improved dynamic Bayesian network model, outputting core root cause nodes and node anomaly coupling relationships, and finally generating a conclusion on the root causes of abnormal production yield in probe devices.

This invention provides a network threat assessment method, medium, and system based on dynamic Bayesian networks, belonging to the field of network security technology. The invention uses a parameter adaptive learning unit with a sliding time window mechanism and a Bayesian online change point detection algorithm to update the conditional probability table parameters. It inputs the topology and parameters into a threat temporal inference model that integrates a liquid neuron layer and a hypergraph matching layer, receives real-time security event evidence, and outputs the posterior probability distribution of nodes. Based on the posterior probability distribution, it calculates the neuron activation regulation function value and dynamically adjusts the liquid neuron parameters. When the posterior probability of a threat node exceeds a preset threat threshold, it executes an iterative belief propagation algorithm through a hierarchical distributed inference collaboration module to generate an attack path probability graph and output the network security assessment result. This solves the technical problem of not being able to simultaneously and accurately model the causal dependencies and high-order collaborative modes of network threat events.

This invention proposes a joint assessment method for fleet mission load and crack state based on Bayesian updates, belonging to the fields of aircraft structural safety, aircraft structural fatigue crack propagation tracking, and aircraft load assessment. The method includes: converting the random load spectrum under different mission histories of the fleet into equivalent constant-amplitude loads using an equivalent load method based on the Paris formula, forming a load level that can be quantified for subsequent analysis; establishing a mission-labeled load library and a fatigue crack damage propagation model; constructing a Bayesian network based on network nodes and interdependencies to build a probabilistic joint assessment architecture for mission load and crack state; using a particle filter algorithm as the inference algorithm for the dynamic Bayesian network to jointly assess the fleet mission load and structural crack state; and testing and verifying the aforementioned network architecture and method in a pre-defined fleet. This invention provides real-time assessment of the load levels of different missions of the fleet, thereby ensuring the safety of the aircraft structure.

This invention relates to the field of electronic digital data processing technology, specifically to a method and system for scoring and predicting risks of cross-chain bridges based on Bayesian networks. The method first addresses the common problems of long-tail distribution and scale inconsistency in cross-chain data through unified discretization, enabling comparison of data from different bridges and chains within the same state space. Then, a static Bayesian network is constructed to probabilistically model multi-dimensional performance indicators and their conditional dependencies, outputting a fine-grained, interpretable posterior probability distribution. Next, entropy-aware scoring is introduced, explicitly incorporating prediction uncertainty into the evaluation process, automatically shrinking the score towards a conservative prior in high-uncertainty scenarios to avoid decision-making risks caused by unreliable predictions. Finally, a dynamic Bayesian network is used to model the logical stage evolution of cross-chain transactions, combined with rolling inference and discounted risk aggregation, to achieve a forward-looking quantification of the temporal propagation and cumulative effects of risk.

This invention relates to the field of safety risk management technology for oil and gas field surface engineering construction, and discloses a method for assessing safety risks in oil and gas field surface construction based on dynamic Bayesian networks. First, it systematically identifies risk factors for all construction processes based on accident causation analysis and a WBS-RBS coupling matrix. Then, it constructs a social network analysis model to quantify the correlation strength of risk factors and determine key risk indicators. Next, it uses the K2 algorithm to optimize the network structure and introduces a time dimension, distinguishing between dynamic and static nodes, and constructs a multi-time-slice dynamic Bayesian network model. Finally, it uses real-time hazard inspection data as evidence input to the model, predicts accident probabilities through forward inference, diagnoses key disaster-causing factors through sensitivity analysis, and outputs risk levels. This method solves the problems of incomplete risk identification, static assessment models, and disconnection from actual construction practices in existing technologies.

The application discloses a power transmission line icing fault prediction method and system based on a Bayesian network, relates to the technical field of fault prediction, and comprises the following steps: acquiring multi-source monitoring data, constructing a unified time series data set; constructing a mechanism constraint rule set, constructing an initial Bayesian network structure, and generating a dynamic Bayesian network model after establishing the state transition dependency relationship between adjacent time slices; inputting the time series data set into the dynamic Bayesian network model, performing posterior probability updating on icing state nodes and hidden variable nodes, obtaining the time series probability distribution of each node, performing icing state recursive prediction of a future time period, establishing the prediction probability distribution of each node in the future time period, performing probability propagation of the dynamic Bayesian network model, and configuring icing fault risk early warning information. The application solves the technical problem that the existing technology cannot accurately predict the icing fault occurrence time and risk distribution, and achieves the technical effects of improving the accuracy of icing fault prediction and early warning.

This invention relates to the field of cold chain storage of fruits and vegetables, and particularly to a method and system for generating fruit and vegetable storage strategies based on multi-source data analysis. The method includes acquiring storage environment sensor data, phenotypic data, and inbound history data; constructing a multi-source heterogeneous time series through timestamp alignment and spatial mapping; calculating the transfer entropy between sequence nodes; selecting feature variables representing the causal relationship of quality deterioration based on directed information flow; establishing a causal topology by constructing a dynamic Bayesian network model using the feature variables as nodes; inputting the model to infer and calculate the posterior probability of deterioration at future time steps; defining the difference between the future time step when the probability first reaches a threshold and the current time as the remaining shelf life; and generating control instructions and outbound strategies based on a preset rule matching the posterior probability and shelf life, and issuing them for execution. This invention effectively removes spurious correlations and achieves causal root cause analysis of deterioration, improves predictive foresight, and establishes a closed-loop control system from early warning to scheduling.

A multi-hazard superimposed instability assessment and advanced control system and method for deep space, belonging to the field of underground engineering safety and intelligent operation and maintenance technology; the system consists of a multi-source synchronous monitoring module, an edge computing and data fusion module, a disaster evolution dynamic assessment and prediction module, and an advanced control execution module connected in sequence; the multi-source synchronous monitoring module includes an array of vibration, seepage pressure, strain, and corrosion sensors. The method involves: installing the multi-source synchronous monitoring module; collecting and fusing multi-source heterogeneous data; constructing a dynamic Bayesian network model to recursively calculate the prior and posterior probabilities of disaster nodes; calculating path trigger probabilities and assessing the severity of consequences, screening out critical paths with high trigger probabilities and severe consequences; locking intervention target nodes based on critical path identification results, establishing a multi-objective optimization model, and quantifying targeted quantitative intervention strategies; implementing precise intervention measures and monitoring response data in real time. This invention can significantly improve the initiative and reliability of safe operation and maintenance in deep engineering.

The application provides a robot visual operation control method and system based on eye tracking, and belongs to the field of robot vision and artificial intelligence. The method comprises the following steps: a head-mounted device with eye tracking function is used to synchronously collect data streams when an operator performs an operation task, wherein the collected data streams comprise eye movement data streams and scene visual data streams when the operator performs the operation task; for the preprocessed data streams, a dynamic Bayesian network is used to extract gaze-intention coupling features; the preprocessed data streams and the gaze-intention coupling features are input into a robot operation model with a double-path attention fusion that has been constructed, so as to obtain online attention results, and the robot performs corresponding operations according to the online attention results. The method of the application can quickly focus on key areas in complex scenes such as occlusion and interference, and improves the task success rate.

A breach loss evaluation method quantifies breach width dynamically, and belongs to the field of dike breach disaster loss evaluation. The dike breach and flood evolution model is used to simulate the flood evolution process of the dike breach, so as to obtain the characteristics of the dike breach flood. A dike breach flood disaster life-economy loss comprehensive evaluation model based on a dynamic Bayesian network is constructed, the downstream disaster loss situation caused by the dike breach flood disaster is quantified, and a breach width and dike breach loss quantitative representation model is linearly fitted through multi-scenario and multi-scale dike breach working condition simulation. The dike breach evolution law is disclosed, the dike breach flood disaster loss situation can be quickly and effectively evaluated, and the systematicness of the dike breach flood disaster loss evaluation method and the timeliness and scientificity of decision making are improved.

This invention discloses an intelligent operation and maintenance management system for data center monitoring equipment based on the Internet of Things (IoT), specifically relating to the field of monitoring equipment operation and maintenance management technology. It aims to solve the problems of data silos, alarm storms, and difficulties in locating the root cause of faults in heterogeneous devices. The system includes a device semantic access module, which transforms multi-source device data into a unified semantic stream through protocol parsing and an adaptive fingerprint database; an edge health assessment module, which performs feature extraction and health calculation at edge nodes, achieving local intelligence and simplified data upload; and a root cause analysis module, which constructs a dynamic Bayesian network based on device topology to perform temporal reasoning on multi-source abnormal events, locate the root cause of faults, and predict risk propagation. This invention achieves plug-and-play functionality, intelligent root cause diagnosis, and proactive early warning for devices, significantly improving the automation level and reliability of data center operation and maintenance.

This invention relates to the field of plant biotechnology and discloses a method for enhancing the stress resistance of tea plant secondary metabolites. The method includes: collecting multi-omics time-series data of tea plants under drought stress to construct a spatiotemporally aligned multidimensional data matrix; using dynamic Bayesian networks to model the dynamic causal regulatory relationships between genes, metabolites, and phenotypes; designing a multidimensional evaluation algorithm integrating network topology and biological effects to screen key regulatory genes; quantitatively predicting their effects on target metabolites such as catechins through in vivo computer perturbation simulations; and finally generating an optimal gene editing or molecular marker-assisted breeding scheme. This invention achieves a breakthrough from association analysis to causal inference, improving the accuracy and efficiency of synergistic improvement of tea plant stress resistance and secondary metabolites.

The application relates to the technical field of data management, and provides an end-to-end metadata closed-loop management method and system, which solves the problem that autonomous operation and maintenance is difficult in the prior art. The method comprises the following steps: acquiring calling chain topology data, time sequence data and temperature and humidity data; performing space-time alignment on the calling chain topology data and the time sequence data to obtain a space-time tensor, and performing physical feature extraction on the temperature and humidity data to obtain a physical vector; inputting the space-time tensor and the physical vector into a space-time graph neural network for dynamic fusion to generate a root cause analysis report; performing time sequence causal inference on the root cause analysis report based on a dynamic Bayesian network to obtain a causal structure diagram; performing decision planning on the causal structure diagram based on a Monte Carlo tree search to obtain a candidate action sequence, and calculating a stability risk coefficient, a resource consumption cost and a system recovery probability; and performing collaborative evaluation to obtain an optimal action sequence and issuing an execution engine. The application can realize end-to-end autonomous operation and maintenance and accurate intervention of microservice performance bottlenecks.

The application relates to the technical field of rail transit, and provides a rail vehicle bogie fault diagnosis method, device and terminal equipment, the method comprising the following steps: acquiring bottom events, middle events and top events of a bogie; constructing a TS polymorphic fault tree model; converting the TS polymorphic fault tree model into a dynamic Bayesian network; determining a constitutive relation safety element of the bogie, and acquiring the probability distribution of the constitutive relation safety element under various working conditions; mapping the probability distribution of the constitutive relation safety element under various working conditions into the state probability of corresponding lower layer events in the dynamic Bayesian network, inputting into the dynamic Bayesian network, and obtaining the dynamic weight of each lower layer event; in the case of bogie failure, calculating the fault probability of each lower layer event of the top event based on the dynamic Bayesian network; and based on the dynamic weight of each lower layer event and the fault probability of each lower layer event, obtaining the analysis result of the fault cause of the bogie failure. In this way, the bogie fault diagnosis efficiency is improved.

This invention relates to the technical field of ship control, and in particular to a method and system for multi-source data interaction processing on ships based on a hub management platform. The method includes: achieving spatiotemporal synchronization of lidar, visual cameras, and millimeter-wave radar through timestamp alignment and coordinate mapping to construct a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting sensor confidence weights by combining ambient light intensity and obstacle surface material; generating incremental obstacle avoidance strategies using a reinforcement learning model, and triggering graded response commands based on risk assessment levels. This invention solves the fusion error problem caused by the spatiotemporal asynchrony of multi-source data in ship navigation, and improves the real-time obstacle avoidance success rate of ships.