69 results about "Conditional probability table" patented technology

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention provides a landslide risk dynamic early warning method and system based on a Bayesian network, and belongs to the field of geological disaster intelligent prediction, and the method comprises the steps: constructing a disaster-inducing factor data set, and screening key factors based on a Pearson's correlation coefficient and an information gain method; an FP-Growth algorithm is further utilized to extract association rules among high-confidence factors, a Bayesian network structure is guided to be optimized, and a Bayesian network model with a causal relationship is constructed; the model supports an incremental learning mechanism based on a newly added landslide sample, can dynamically update a conditional probability table, and realizes landslide probability prediction and risk grade division in combination with a Bayesian forward reasoning result. The method has the advantages of being high in causal reasoning ability, high in model structure expression ability, excellent in prediction precision and capable of supporting real-time updating and risk partition, and is suitable for an intelligent risk assessment and early warning system for landslide disasters.

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.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses an uncertainty reasoning state scoring and early warning system for gear machining equipment, and relates to the technical field of gear machining monitoring. Data are collected in real time through an industrial Ethernet, time synchronization is realized based on an IEEE1588PTP protocol, and a data cleaning sub-module is designed to repair an abnormal value and keep a record; the method comprises the following steps of: extracting 128-dimensional potential features of a vibration signal by adopting a variational auto-encoder, fusing a temperature drift coefficient and a current harmonic component, quantifying feature uncertainty through Monte Carlo dropout, and marking low-reliability features; and constructing a Bayesian network containing 5 state nodes and 12 observation nodes, calculating a posterior probability through a Markov chain Monte Carlo algorithm, dynamically adjusting a conditional probability table, and fusing multi-feature scores. According to the method, the state evaluation accuracy and the early warning timeliness are improved, the resource consumption is optimized, full-link monitoring is realized, the fault handling efficiency is enhanced, and reliable support is provided for precision manufacturing.

The invention provides a construction interruption risk reasoning and plan generation method and device. The method comprises the following steps: acquiring multiple types of risk factors influencing construction interruption and a historical data set; taking a Bayesian information criterion as a scoring function, and constructing a directed acyclic graph structure based on the scoring function, a historical data set and a constraint set determined by domain knowledge; the constraint set is used for limiting whether edges between nodes exist or not in the construction process of the directed acyclic graph structure; based on the directed acyclic graph structure and the historical data set, a conditional probability table of the directed acyclic graph structure is determined, and the directed acyclic graph structure and the conditional probability table form a Bayesian network model; determining the risk probability of the target construction interruption event based on a Bayesian network model; and optimizing a preset construction plan combination based on the risk probability, and determining an optimal plan combination. According to the directed acyclic graph structure, objective data and domain knowledge are considered, and the accuracy of subsequent risk reasoning and plan generation is improved.

The invention discloses an electricity consumption anomaly detection method and system based on a Bayesian model, and the method comprises the steps: obtaining multi-mode electricity consumption data, carrying out the preprocessing of the collected multi-mode electricity consumption data, carrying out the preprocessing of an innovative preprocessing frame which comprises dynamic mode alignment and multi-mode missing value processing, and achieving the deep cooperative processing of data through a progressive architecture; initializing a Bayesian network model, defining nodes and a structure, and initializing a conditional probability table; learning the structure and parameters of the Bayesian network by using the sample data, and adjusting the structure and parameters of the network; and performing multivariable joint probability fault reasoning by using the trained dynamic Bayesian network DBN, namely predicting and classifying new data. According to the method, a closed-loop technology covering a sensing-inference-decision link of electricity consumption anomaly detection is formed, and compared with the prior art, the method has remarkable advantages.

The invention discloses a Bayesian reasoning method for high-voltage power supply reliability under common cause failure influence, and belongs to the technical field of high-voltage power supply reliability analysis. Constructing a dynamic fault tree of the high-voltage power supply; acquiring the proportion of common cause failure in each pair of bottom event failures, and collecting evaluation suggestions for the proportion of the bottom event to construct a fuzzy matrix; determining common cause failure factors of common cause failure of each pair of bottom events; the dynamic fault tree of common cause failure is converted into a discrete time Bayesian network, and a Bayesian network graph, a conditional probability table and a marginal probability table are obtained respectively; and performing bidirectional reasoning on the dynamic reliability of the high-voltage power supply, and calculating reliability and maintainability indexes of the high-voltage power supply in each time period. According to the method, the common cause failure factor is determined through the fuzzy theory, and the dynamic fault tree considering common cause failure is mapped into the Bayesian network for reliability analysis, so that the problems that the common cause failure factor is difficult to determine and the reliability analysis calculation amount is too large are solved, and the high-voltage power supply reliability analysis precision and calculation efficiency are effectively improved.

This invention discloses a method for fire risk assessment of LNG storage tank areas based on Bayesian networks, comprising the following steps: S1, determining the initial accident tanks and fire scenarios in the LNG storage tank area, and calculating the intensity of thermal radiation generated by the fire; S2, determining the safety barriers set for each target tank, evaluating the performance parameters of each safety barrier, and calculating the failure probability of each target tank under thermal radiation; S3, constructing a Bayesian network model, and setting the conditional probability table for each node based on the failure probability of each target tank under thermal radiation; S4, determining the probability of a domino effect accident through forward reasoning of the Bayesian network; and identifying the most likely accident propagation sequence through backward reasoning of the Bayesian network, providing decision support for emergency response.

The application discloses a fault diagnosis method and system of a power centralized communication control device, which is used for accurately identifying cascading faults to improve the safety and stability of a power system. The method comprises the following steps: obtaining operation data of the power centralized communication control device, and extracting a frequency feature set from the operation data; calculating the correlation strength between each variable in the frequency feature set by using a conditional probability table, screening out a key feature vector set, and generating an interaction topology between different devices in the power system; calculating the propagation path and propagation speed of each key feature vector in the interaction topology according to a path reasoning algorithm, and screening out a candidate fault propagation path set based on the propagation path and propagation speed; calculating the fault propagation probability of the candidate fault propagation path, determining a cascading fault propagation feature set, constructing a high-dimensional space classification boundary according to the cascading fault propagation feature set, and performing fault mode classification to obtain a fault diagnosis result.

The invention provides an electric power system state evaluation and early warning method based on a dynamic Bayesian network, which relates to the technical field of electric power, and comprises the following steps: obtaining time sequence historical data and topological relation data of an electric power system, extracting time sequence characteristics from the time sequence historical data based on a hierarchical sliding window method of an alarm level, and calculating the state of the electric power system according to the extracted time sequence characteristics; on the basis of the topological relation data, constructing a domain structure in a topological conversion mode; performing dynamic Bayesian network modeling according to the time sequence characteristics and the domain structure; according to the dynamic Bayesian network, constructing a conditional probability table and a state transition matrix by using a cross-domain prior probability in combination with a maximum likelihood estimation method; calculating a prediction state of the domain structure based on the conditional probability table and the state transition matrix in combination with monitoring data of each node in the domain structure; and performing power system state evaluation and early warning according to the prediction state of each domain structure in the power system. According to the invention, a new methodology is provided for fault prediction and operation state management of the intelligent power grid.

This invention discloses a method for optimizing the operating parameters of a vacuum adsorption system based on transfer learning. Specifically, the method includes: aligning historical and current adsorption condition data by time and dimension to form a sequence of operating condition features; extracting material, roughness, load, pressure, flow rate, leakage, and cycle time features to form a set of node vectors; constructing an initial structure of a semi-naive Bayesian network to generate a set of candidate parent sets; performing parent set competition explicitness calculations to determine the dominant parent set identifier; statistically analyzing the evidence quantity of historical, current, and conflicting samples to generate a local evidence concentration sequence and form a conditional probability table; performing parameter inference to generate vacuum pressure values, pressure holding time values, valve switching time values, and leakage compensation amplitude values; and performing constraint verification to output a set of operating parameters. This invention achieves adaptive optimization of the operating parameters of a vacuum adsorption system under the combined influence of historical and current operating conditions.

The invention discloses a premature infant retinopathy risk prediction method and system based on a Bayesian network. The method comprises the following steps: S1, data acquisition and grouping; s2, performing quality control and standardization processing on clinical variables, and preprocessing microbiome data; s3, based on a structure learning algorithm, constructing a directed acyclic graph Bayesian network model which jointly reflects a dependency relationship between clinical variables and microbiome variables, optimizing the structure in combination with clinical prior medical knowledge, generating a conditional probability table of each variable, and realizing multi-factor joint risk reasoning and interpretation; s4, model training and performance evaluation; and S5, inputting data, analyzing, outputting the risk probability of occurrence of ROP of the child patient, and visually displaying the risk probability. By integrating multi-dimensional clinical variables and intestinal microbiome characteristics, a Bayesian network model which is reasonable in structure, high in interpretability and high in prediction accuracy is constructed, and early layered prediction and individualized intervention suggestions on the retinopathy risk of the premature infant are achieved.

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.

The invention discloses a distribution network new energy facility disaster chain coupling transmission model construction method, and relates to the technical field of novel power system disaster modeling, simulation analysis and risk assessment, and the method comprises the steps: obtaining the type, intensity and influence range of a disaster faced by a distribution network new energy facility under a typhoon and rainstorm coupling scene, and recognizing a key disaster chain scene element; determining key nodes in the disaster chain according to the identified elements; constructing a topological relation of a disaster chain network based on the key nodes to form a disaster chain network structure; constructing a conditional probability table for the constructed topological relation; and simulating a disaster transmission process according to the constructed model, and introducing case backtracking verification to evaluate the accuracy of the model and optimize model parameters. According to the method, the coupling fault transmission process of the distribution network new energy facility under the typhoon and rainstorm coupling scene is effectively simulated, and accurate risk assessment and decision support can be provided for formulation of disaster prevention and reduction measures and optimization of emergency response strategies.

The invention provides a human activity interference risk assessment method and system based on a Bayesian network, and relates to the field of ecological environment protection, and the method comprises the steps: obtaining risk assessment data corresponding to a preset risk factor in a to-be-assessed geographic region; determining risk state levels corresponding to different risk evaluation data according to a preset risk state level interval; matching risk probability distributions corresponding to different risk factors from a preset conditional probability table according to the risk state levels; and loading the risk probability distribution and the risk factor to a preset Bayesian network model for probabilistic reasoning calculation to obtain posterior probability distribution of the to-be-evaluated geographic area under one or more risk probability levels, and then generating and outputting a risk evaluation result of human activity interference in the to-be-evaluated geographic area. By implementing the method, the accuracy and reliability of ecological risk assessment can be improved.

The application discloses a constellation core network test fault positioning method and device, the method comprises the following steps: when the test task of the constellation core network fails, the path label chain of the test task is extracted as a fault label path; a directed dependency graph representing a fault propagation path is constructed according to the directed dependency relationship between network elements in the fault label path; a Bayesian network is constructed with the fault phenomenon corresponding to the fault label path as an observation node, the directed edges in the directed dependency graph as propagation nodes, and the network elements in the directed dependency graph as root cause network elements; a conditional probability table is constructed according to the causal relationship between the root cause network element anomaly, the propagation node anomaly and the fault phenomenon in the Bayesian network; the multi-dimensional evidence fusion score of each root cause network element is calculated according to the conditional probability table; and whether the root cause network element with the highest multi-dimensional evidence fusion score is a fault node is judged according to the confidence of the root cause network element with the highest multi-dimensional evidence fusion score. The application has high accuracy and high efficiency in fault positioning.

The application discloses a learner intelligent diagnosis method based on a Bayesian network, and belongs to the technical field of intelligent education. Knowledge points are modeled as binary random variables, a Bayesian network with a conditional probability table is constructed, and a bidirectional weighted graph is constructed through forward sampling and mutual information; a triple encoding Transformer that fuses node centrality, the shortest weighted path and edge encoding is used to generate node embedding; from the perspective of information theory, intelligent diagnosis is equivalent to a maximum decoding information path search problem, and the optimal diagnosis path is searched based on node embedding; through offline construction of a K-neighbor graph index and online greedy search, efficient diagnosis is realized with one training and multiple inferences. The application can accurately model knowledge level dependency, provide an interpretable diagnosis conclusion with causal tracing capability, and significantly improve the computational efficiency in multiple diagnosis scenarios, and can be used for personalized learning diagnosis and teaching intervention in an intelligent education system.

This invention discloses a method for coal mine terminal risk scoring based on Bayesian networks, comprising the following steps: Step 1: Collecting coal mine terminal risk data to form a terminal risk dataset; Step 2: Extracting risk variable nodes, barrier variable nodes, and terminal scoring nodes, constructing a Bayesian network, and forming a node conditional probability table; Step 3: Performing deontization, triangulation, and clique partitioning to form a connection tree and a partition set; Step 4: Calculating the probability estimate of clique nodes and the barrier state description term; Step 5: Constructing propagation messages using an improved Shafer-Shenoy algorithm to form a penetrating residual component; Step 6: Performing state enhancement marginalization to form a partition set propagation message; Step 7: Forming the terminal residual posterior risk distribution; Step 8: Forming the coal mine terminal risk scoring result. This invention improves the ability to characterize coal mine terminal residual risk and the reliability of risk scoring.

The embodiment of the application discloses a kind of based on FG-DBN's multi-electric aircraft airborne system reliability analysis method, it is related to reliability design field, can be in fuzzy uncertain condition, to the complex polymorphic system in multi-electric aircraft airborne equipment is carried out dynamic reliability analysis.This application includes: the DBN model of complex polymorphic system in airborne equipment is established, the node in DBN model corresponds the failure state of component in complex polymorphic system;Fuzzy gradient function model containing fuzzy support radius is established, fuzzy gradient function model includes: fuzzy membership model of node in corresponding time slice and state transition matrix corresponding to failure mode;The conditional probability table is established for the failure relationship between nodes in DBN model;The output result of fuzzy gradient function model containing fuzzy support radius and the conditional probability table established are used, the node of weak link in complex polymorphic system is identified and reliability is analyzed.

This invention discloses a multi-parameter status monitoring and fault early warning system for transformer substations based on the Industrial Internet, belonging to the field of intelligent operation and maintenance technology for industrial equipment. It includes a data acquisition and quality quantification unit, used to collect monitoring data from sensors and quantify the quality of the monitoring data in real time to generate a data quality vector; a dynamic weight optimization unit, used to receive the data quality vector and the fault posterior probability distribution output by the causal reasoning unit, and construct the current state of the decision-making environment based on the data quality vector and the fault posterior probability distribution, and then output an evidence weight vector through a built-in reinforcement learning agent; and a causal reasoning unit, used to receive the evidence weight vector and use the evidence weight vector to weight the conditional probability table of the built-in dynamic Bayesian network model, thereby updating and outputting the fault posterior probability distribution. This invention significantly improves the diagnostic system's resistance to data noise and interference.

The invention relates to an auxiliary driving accident tracing method fusing feature screening and a dynamic Bayesian network, and the method comprises the following steps: S1, constructing a mixed training data set containing a positive sample of accident data and a negative sample of normal driving data, and quantitatively screening out a key data field having correlation with the accident from massive signals; s2, constructing a generative directed edge pointing to objective performance from subjective causes, and constructing a double-layer dynamic Bayesian network skeleton; s3, constructing a special data set only containing the accident data, discretizing key data fields, inputting the discretized key data fields into a DBN empty skeleton, and outputting a prior DBN model filled with a complete conditional probability table; and S4, outputting a causal chain of'objective performance caused by subjective cause 'with the maximum probability. According to the invention, refined and automatic distinguishing of accident liabilities is realized, and the method has great technical value and social significance for improving traffic safety, perfecting an auxiliary driving system and clarifying man-machine responsibilities in the future.

This invention discloses a method for predicting rural ecological space based on Bayesian networks. The method includes: establishing a rural ecological space evaluation index system based on selected suitable factors; determining the time points involved in the Bayesian network model for rural ecological space prediction; acquiring basic data and creating a thematic map of rural ecological space evaluation indicators using preprocessed basic data; constructing a Bayesian network model for rural ecological space prediction by combining the causal relationships of the rural ecological space evaluation indicators; using the Bayesian network model for parameter learning to obtain a conditional probability table; and using the inference engine of the Bayesian network model to predict rural ecological space. This invention, based on the mechanism of action of factors influencing the evolution of rural ecological space and the game relationship between different functional spaces, employs Bayesian networks to solve the problems of the randomness of human activities and the uncertainty of dynamic changes over time in the evolution of rural ecological space, thus achieving the prediction of rural ecological space and improving the accuracy of the prediction results.

The invention belongs to the technical field of fault analysis, and particularly relates to a crane fault analysis method based on a knowledge graph, which comprises the following steps of: acquiring sensor data, vehicle conditions, working environments and geographic positions of a crane, inputting the sensor data, the vehicle conditions, the working environments and the geographic positions into a fault diagnosis network model, and obtaining diagnosis and prediction information of crane faults. The fault diagnosis network model construction method comprises the following steps: constructing a fault tree which comprises a top event, a middle event and a bottom event; converting the fault tree into a knowledge graph, mapping a top event into a failure mode entity, and mapping a bottom event into a fault root cause entity; integrating fault case data, and adding a fault case entity, variable entities such as a vehicle condition, an operation environment and a geographic position, and a sensor characterization entity; a naive Bayesian network is constructed after a knowledge graph relationship type is defined, nodes represent various entities and sensor parameters, and edges represent condition dependency relationships; on the basis of historical data discretization variables, Bayesian estimation is adopted to generate a conditional probability table;

The invention discloses an autonomous underwater vehicle cluster task fault diagnosis method, and relates to the technical field of underwater unmanned system reliability diagnosis. The method comprises the following steps: constructing a multi-level fault analysis tree oriented to task failure based on task execution logic and a multi-agent cooperative relationship; constructing an autonomous underwater vehicle cluster task fault reasoning model based on a Bayesian network according to the multilevel fault analysis tree; calculating and calibrating a prior probability and conditional probability table of each node in the Bayesian network by combining literature data, historical operation data and domain expert knowledge of the autonomous underwater vehicle cluster; and inputting the observed system state or task deviation as evidence into the fault reasoning model, and calculating the fault occurrence probability of each node in the network by using bidirectional reasoning. According to the method, the spanning from monomer analysis to cluster task level evaluation can be realized, the diagnosis efficiency is improved, the diagnosis integrity and practicability are enhanced, and the method is suitable for various AUV cluster task scenes.

The invention discloses a conditional probability modeling method and device based on an improved Bayesian network, and relates to the field of data processing. In the method, Bayesian network nodes are constructed, and the Bayesian network nodes comprise child nodes and father nodes; obtaining a scoring result of each output state of each father node by a plurality of experts, calculating a prior probability value of each output state of each father node according to the scoring result, and establishing a prior probability table of each father node; according to the prior probability value of each output state of each father node, obtaining conditional probabilities of different output states of child nodes when each father node independently acts on the child nodes, and constructing a preconditional probability table; on the basis of the preconditional probability table, introducing a weight vector of each father node to obtain a conditional probability table; and calculating the posterior probability of each output state of each sub-node based on the conditional probability table and the prior probability table. By implementing the technical scheme provided by the invention, an efficient posterior probability calculation method is provided, and the modeling efficiency is improved.

The present application relates to medical care informatics, and discloses a kind of based on causal inference's chronic disease intervention effect evaluation and prediction method, comprising: obtaining long-period medical record to construct physiology and intervention sequence matrix, the conditional probability value of each time observation step intervention occurrence is calculated, the clinical contraindication threshold matrix corresponding to physiological state is determined, if physiological state falls into forbidden interval, the probability reciprocal calculation path is blocked, the intervention frequency deviation residual in historical stationary window is extracted to cover time-varying confounding adjustment weight, according to weight, sequence matrix is resampled to generate quasi-state equilibrium sequence, mapping output evolution track, the present application uses residual compensation mechanism to hedge the calculation divergence risk caused by the probability extreme value induced by guideline constraint, guarantee data time sequence integrity under critical condition, eliminate survivor bias.

This invention discloses a dynamic risk assessment method and system for storage tanks, comprising the following steps: S1, identifying potential risk sources of the storage tank, taking tank leakage as the assessment object; S2, inputting the potential risk sources and their possible consequences into a fuzzy Bow-Tie model to form a preliminary qualitative analysis of consequences and measures; S3, transforming the Bow-Tie model into a Bayesian network based on mapping relationships; S4, having experts score the corresponding risk sources and converting the scores into prior probabilities of the root nodes of the Bayesian network, determining the weights of each expert using the analytic hierarchy process (AHP), and converting the expert evaluation results into fuzzy probabilities to obtain the prior probabilities of the root nodes and determine the conditional probability tables of other nodes; S5, calculating the posterior probabilities and analyzing and determining the priority relationships of failure modes; S6, providing corresponding risk control measures based on the priority judgment results. This method realizes management from risk source identification to control measure output, overcoming the problem that traditional methods cannot cope with complex dynamic environments.

The present application relates to the field of Bayesian network, and discloses a method, device, equipment and medium for explosion case scene reconstruction and suspect judgment, which can construct a Bayesian judgment network of the explosion case according to the condition elements, scene elements and consequence elements of the explosion case, generate a prior condition probability table of the Bayesian judgment network based on triangular fuzzy numbers and mean area method, take multiple elements, the correlation between different elements, part of criminal facts, preliminary assumptions, the occurred scene and the unoccurred scene obtained by surveying and analyzing the central scene as reasoning conditions, input the reasoning conditions into the Bayesian judgment network, make the Bayesian judgment network perform scene reconstruction and suspect judgment according to the reasoning conditions and the prior condition probability table, and obtain the coarse-grained criminal facts, fine-grained criminal scene, behavior and investigation suggestions generated and output by the Bayesian judgment network. The present application can effectively improve the intelligent investigation level and reduce the human resource consumption of investigators.

This invention discloses a method for bridge expansion joint status identification and early warning based on Bayesian data fusion, comprising: S1, constructing a disaster-causing factor system and a failure cause database for expansion joints; S2, collecting multi-dimensional data of expansion joints; S3, preprocessing and extracting features from the collected multi-dimensional data; S4, constructing a Bayesian network model based on a fault tree; S5, fusing multi-dimensional data to determine the prior probability of the Bayesian network model; S6, constructing a conditional probability table for each node of the Bayesian network based on maximum a posteriori estimation and the analytic hierarchy process; S7, quantifying the failure probability of expansion joints through forward inference using the Bayesian network, classifying risk levels and risk warnings; and screening the main disaster-causing factors through backward inference using the Bayesian network. This invention integrates multi-dimensional data and multi-disciplinary theoretical methods, solving the problems of single data dimensions, low fusion efficiency, and insufficient early warning accuracy in existing technologies, and significantly improving the accuracy of expansion joint damage identification and the reliability of risk assessment.