593 results about "Bayesian network" patented technology

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention discloses a multi-modal fusion lithium iron phosphate battery thermal runaway early warning method and system, and the method comprises the steps: collecting multi-source heterogeneous data, such as temperature, voltage, gas concentration and shell strain pressure, in real time through deploying a heterogeneous sensor network, and carrying out the noise reduction and time sequence feature extraction through employing a sub-linear time low-rank approximation algorithm of a Hankel matrix; constructing a cross-modal feature association network by applying a secondary time algorithm of a maximum weight sparse subgraph problem, inputting a fused feature vector into a Bayesian network health degree evaluation model for probabilistic reasoning calculation to obtain a battery health degree score and a thermal runaway risk level, generating a graded early warning signal through multi-level early warning threshold comparison, and performing early warning on the battery health degree score and the thermal runaway risk level. And corresponding prevention and control suggestions are matched. The method solves the technical problems that single physical quantity monitoring is difficult to comprehensively reflect the complex change in the battery and the response delay of a centralized processing architecture causes the early warning lag, and achieves the timely capture and accurate early warning of the early weak characteristics of thermal runaway.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention relates to the technical field of tunnel construction safety monitoring, and discloses a tunnel construction safety evaluation method and system based on multi-dimensional data fusion analysis, and the method comprises the steps: constructing a digital twinborn model, and building an initial twinborn body comprising a BIM model and a three-dimensional geologic model; performing mechanical parameter inversion and dynamic updating on the three-dimensional geologic model through real-time multi-dimensional monitoring data to obtain a dynamic twinborn body; performing fusion analysis on the monitoring data by adopting a fusion algorithm, and outputting a safety risk coupling evaluation result; based on an evaluation result, combining with surrounding rock mechanical response of dynamic twinborn simulation, and adopting fuzzy comprehensive evaluation to output a safety level; and performing risk traceability and positioning main risk factors through a Bayesian network based on the security level, generating a customized support decision through case reasoning, and performing visual display and early warning in a digital twinborn model. The system corresponds to the method. According to the invention, dynamic accurate evaluation and intelligent decision support of tunnel construction safety are realized.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention discloses a reservoir group-lake system flood control risk adaptive analysis method and system. The method comprises the following steps: acquiring a basin hydrological sequence and a pre-configured physical causal constraint matrix, and constructing a VineCopula joint distribution and initial Bayesian network conforming to physical topology according to the basin hydrological sequence and the pre-configured physical causal constraint matrix; risk sensitive weights of the sample points are calculated based on the Bayesian network, and weighted sampling density inclining towards the high-risk area is constructed; generating a self-adaptive scene set based on the density, solving an optimal scheduling scheme, feeding back a scheduling result to the Bayesian network to update parameters, carrying out closed-loop iteration of perception-sampling-scheduling-learning until risk assessment is converged, and determining a comprehensive flood control risk assessment result of the system based on the converged risk factor Bayesian network. According to the method, the problems of poor physical interpretability and scarcity of extreme risk samples of a traditional model are solved, and accurate capture and quantification of extreme risks of a complex flood control system are realized.

The invention discloses a national secret algorithm data encryption transmission method and system. The method comprises the steps of obtaining to-be-encrypted data and network parameters, establishing a Bayesian network probability ablation model, performing Monte Carlo sampling ablation national secret encryption and evaluating attack risks, and generating a probability security encryption strategy; and extracting a Brinell feature set, constructing a long and short-term memory network time prediction model, and optimizing by using a simulated annealing algorithm to obtain an optimal encryption parameter configuration sequence. Generating a key pair according to the sequence and SM2, establishing a shared key by means of an elliptic curve Diffie-Hellman protocol, deriving an SM4 session key through SM3, and establishing a hybrid encryption key system; constructing a teacher and student network model, optimizing multi-thread scheduling through adversarial distillation training and a retrieval enhancement technology, and generating a multi-thread parallel encryption architecture; and network parameters are monitored in real time, a reinforcement learning adaptive decision engine is constructed, a strategy is dynamically adjusted, and adaptive encryption transmission is completed. According to the invention, the optimal balance between the security and the efficiency in the data encryption transmission process is realized.

The invention discloses a target range federated cross-domain threat research and judgment method and system, and aims to solve the problems of difficulty in cross-domain threat association analysis, entity splitting and low research and judgment precision caused by incapability of sharing original security data among multiple target ranges. The method comprises the following steps: each sub-target range locally performs structured processing and high-order semantic coding on original security data to generate a standardized research and judgment intermediate representation; the central coordination node executes cross-domain entity disambiguation based on the intermediate representation, constructs a time sequence event atlas, fuses multi-source evidences through a Bayesian network, and outputs a global threat research and judgment conclusion; and the result is transmitted back to a related target range according to an authority strategy to form closed-loop feedback. The system comprises a research and judgment agent unit and a central coordination node which are deployed in each sub-target range and are used for respectively realizing local feature extraction and collaborative reasoning. According to the method, on the premise of ensuring that the original data is not out of the domain, cross-domain accurate reduction and cooperative defense of complex threats such as an APT attack chain are realized.

The invention discloses an intelligent scheduling method for unmanned workshop production, and the method comprises the steps: collecting the feature data of task orders, equipment loads and the like in real time, carrying out the normalization and feature dimension reduction processing, and constructing a standardized task state vector; constructing a task-resource-state causal graph based on a Bayesian network, and identifying and quantifying key factors influencing the scheduling performance; in combination with causal reasoning and anti-factual reasoning, multi-strategy generation and screening under new tasks and abnormal working conditions are realized, and online fine adjustment is performed on selected strategies through a reinforcement learning model; feedback data are collected after scheduling is executed, the causal model is dynamically corrected, continuous self-evolution and generalization ability improvement of the strategy are achieved, and the production flexibility, the resource utilization rate and the scheduling robustness of an automatic workshop can be effectively improved.

The invention discloses a flood type landslide disaster monitoring and early warning method, and belongs to the technical field of geological disaster prediction. The method comprises the following steps: step 1, acquiring a flood type landslide disaster case in which a rainstorm event and a landslide event coincide in time and space, collecting landslide factor data, environment data, monitoring data and historical landslide data, and constructing a historical database; step 2, constructing and training a Bayesian network model based on a historical database; step 3, acquiring landslide monitoring data and preprocessing the landslide monitoring data; step 4, training an LSTM time sequence prediction model; 5, inputting the real-time monitoring data into the LSTM model, and predicting to obtain future landslide monitoring data; and step 6, inputting future monitoring data into the Bayesian network to obtain a slope instability probability based on a future trend. According to the invention, through organic fusion of the Bayesian network and the LSTM, dynamic prediction and real-time response of the landslide instability probability are realized, and timeliness, accuracy and robustness of early warning are significantly improved.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.

The invention provides an intelligent identification and alarm method for respiratory suppression events in an anesthesia revival period, which comprises the following steps of: continuously acquiring high-frequency physiological data such as respiration, blood oxygen and electrocardio of a patient through multi-channel equipment, and establishing a dynamic causal network model fusing medical priori knowledge and clinical guidelines after standardized processing and feature extraction; a Granger causal test and a dynamic time warping algorithm are combined, a significant causal relationship among key physiological parameters is dynamically identified, a causal network structure is updated in real time, a causal analysis result is further input into a time sequence Bayesian network, calculation of a respiratory suppression event occurrence probability and reasoning path tracing are realized, and the probability of occurrence of a respiratory suppression event is calculated. According to the method and the system, the probability score is calculated, an interpretable medical logic evidence chain and thermodynamic diagram visualization are automatically generated, and if the probability score exceeds the limit, multi-mode alarm and data locking are synchronously triggered, so that the timeliness, intelligence and interpretability of respiratory suppression detection are improved, and clinical precise intervention is facilitated.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention discloses an ecological shoreline diagnosis method and system based on hydrological-biological communication, and is applied to the technical field of water environment ecological restoration and shoreline health assessment. Comprising the following steps: dividing intertidal zones of a shoreline research area, and generating uniformly distributed sampling points in each tidal zone; extracting a plurality of indexes related to hydrological and biological connectivity, and constructing a comprehensive hydrological-biological connectivity state index through principal component analysis; dam feature data of a shoreline area are obtained, correlation analysis and collinearity screening are carried out, and key shoreline features are screened through a random forest algorithm; constructing a mechanism connectivity index based on a circuit theory; constructing and training a Bayesian network prediction model; and performing connectivity state diagnosis, attribution analysis and restoration scheme effect simulation on the target shore section by using a Bayesian network prediction model. By coupling hydrological and biological connectivity, static element evaluation is changed into dynamic ecological process diagnosis, and dominant factors are accurately positioned.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention relates to the technical field of data processing, in particular to a BN-based UHPC mix proportion data analysis method and a strength prediction system.The intelligent level of ultra-high performance concrete mix proportion design is improved by introducing a Bayesian network model and a multi-objective optimization algorithm, and the method comprises the steps that firstly, an enhanced feature set is generated through feature engineering; a nonlinear relationship and a cross-level interaction effect among material parameters are fully excavated, and the characterization capability of the model on a complex material system is enhanced; secondly, the Bayesian network model is combined with causal reasoning and a hierarchical regularization strategy, so that the overfitting risk is reduced while the compressive strength prediction precision is ensured; in the multi-objective optimization link, through dynamic weight adjustment and Pareto frontier search, carbon emission and strength requirements are effectively balanced, and candidate schemes with low carbon and excellent mechanical properties are output; and finally, dynamic evaluation and risk analysis are carried out to further screen out a mix proportion with high stability and strong feasibility, and a reliable decision basis is provided for engineering practice.

The invention provides a state management and anomaly backtracking method and system for a multi-agent collaborative task, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining interaction data, state data and an execution log, building an initial task knowledge graph based on an ontology model, building a bidirectional causal chain through a Bayesian network, and forming a dynamic knowledge graph; and when an exception is detected, analyzing a state transition rule, identifying propagation nodes, constructing an exception propagation sub-graph, generating an influence chain, determining a recovery priority and triggering state rollback. According to the method, an abnormal source can be quickly traced, abnormal diffusion is effectively controlled, and the system robustness and the task recovery efficiency are improved.

The invention discloses an underground pipe network water quality sudden change detection system and method. Comprising a multi-parameter distributed sensing network module, a data transmission module, a data processing and analyzing module, a space-time traceability positioning module and a user interaction and alarm module, wherein the multi-parameter distributed sensing network module comprises various types of water quality sensors and hydraulic sensors; the data acquisition module is used for acquiring water quality parameter data and hydraulic parameter data of each node in real time; the invention relates to the technical field of water quality monitoring. According to the underground pipe network water quality abrupt change detection system and method, multi-dimensional data are collected in real time through a distributed anti-interference sensing network, and an intelligent closed loop from data collection to abrupt change recognition is constructed in combination with'mixed transmission architecture + improved Bayesian network fusion analysis + LSTM-AE deep learning detection '; a complex sudden change mode of multi-parameter cooperative change can be rapidly identified, and risk expansion caused by monitoring lag or shallow layer analysis is avoided.

The invention relates to an AEB triggering method for reducing false triggering in a curve scene, and the method comprises the steps: firstly constructing a lane line recognition and lane geometric modeling module, forming a stable lane boundary and a center line, building a Frenet coordinate system, and completing the judgment of the lane attribution of a target vehicle and the compensation of the curve distance; performing out-of-control stage identification on an overline target after an overline event is triggered, and generating a trajectory beam and a three-dimensional dynamic occupation envelope under the constraint of a stage label to obtain a future influence area of the target; a tree-enhanced Bayesian network is introduced into the risk inference layer to describe a dependency relationship among multiple risk variables, and collision point grid posteriori distribution and a high-risk duration window are output; a braking feasible region is constructed by combining a forward collision avoidance demand and a backward rear-end collision prevention constraint at a braking decision-making layer, and an impact rate constraint and a damage cost are introduced to realize AEB graded triggering and braking instruction optimization, so that an out-of-control intrusion risk can be inhibited in advance, and the false triggering probability can be reduced.

The invention relates to an underwater unmanned vehicle state reconstruction method under incomplete information. The method comprises the following steps: obtaining multi-source sensor data of an underwater unmanned vehicle, and carrying out space-time alignment and data fusion; constructing a state prediction model, taking environment characteristics and a sensor sequence as input, introducing physical constraint loss and a dynamic forgetting mechanism based on a marine environment, and predicting a motion state; state information missing detection and classification are carried out by using a multi-source evidence fusion missing detection model and a Bayesian network-based missing reason classification method in combination with a dynamic threshold adaptive mechanism; constructing an adaptive state space model of the marine environment, adopting an adaptive reconstruction strategy according to a missing type, and realizing state reconstruction under incomplete information in combination with a layered reconstruction framework and marine dynamics constraint optimization; and carrying out reliability verification on the reconstruction result, and if the verification is not passed, carrying out feedback optimization through reinforcement learning. Compared with the prior art, the state reconstruction problem of the UUV in a complex marine environment and under the condition of information loss is solved, and the reconstruction precision, robustness and adaptability are improved.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The embodiment of the invention discloses an alarm method and device based on a telemedicine conference, a medium and a program product. The method comprises the following steps: performing data extraction and classification on consultation records of a telemedicine conference, collected historical medical records and environment detection records, and obtaining medical data and context awareness data of a patient; acquiring disease probability distribution based on the noise gate Bayesian network and the medical data; acquiring a feature vector matrix for the context awareness data and the disease probability distribution through an interface layer; acquiring a disease type, a severity parameter and an emergency degree parameter of the patient based on the connection heuristic network and the feature vector matrix so as to determine whether the patient has a health risk, and if yes, selecting an optimal receiver; and sending alarm information to the optimal receiver. The method can dynamically adapt to complex and changeable medical situations and environmental factors in combination with the use of a remote medical conference, and carries out intelligent alarm, so as to improve the accuracy and response speed of patient health risk identification.

The invention relates to the technical field of natural disaster risk assessment, in particular to a multi-disaster disaster-inducing factor internal correlation analysis method, which comprises four steps of data preprocessing, Bayesian network correlation model construction, space-time dynamic evolution analysis and risk early warning threshold model construction. And deep coupling analysis of disaster-inducing factors of multiple disasters such as mountain torrent-debris flow and the like is realized. The method comprises the following steps: firstly, collecting 17 disaster-inducing factor data such as terrain and rainfall, performing dimensionality reduction through kernel principal component analysis, and calculating a dynamic weight by adopting an entropy evaluation method; then constructing a Bayesian network model to quantify conditional probability association; introducing a space-time attention mechanism to optimize a multi-scale coupling weight; and finally, establishing a coupling risk early warning threshold value and outputting a relevance intensity matrix. According to the method, the problem that a traditional single-disaster analysis method cannot capture the space-time linkage effect of disaster-inducing factors is solved, the risk early warning accuracy of multiple disasters is improved, and scientific decision support is provided for regional disaster prevention and reduction.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

The invention discloses a crane fault diagnosis method and system based on data driving, and the method comprises the steps: collecting the data of a PLC and a multi-source sensor of a crane, carrying out the preprocessing, and inputting a prediction model with the fusion of multi-scale causal convolution and an attention mechanism, so as to obtain a feature value prediction sequence; then calculating a residual error between a prediction sequence and an actual measurement sequence, modeling by using a first-class support vector machine, and triggering third-class early warning; based on the constructed Bayesian network, inputting the early warning evidence and updating the posterior probability, and outputting a Top-N fault reason; and finally, a risk score is calculated by integrating the posterior probability, the residual amplitude and the abnormal frequency, grading is carried out, and a diagnosis result and a disposal suggestion are pushed to a user terminal. According to the scheme, accurate diagnosis of complex coupling faults can be realized, 'beforehand 'early warning is realized, and unplanned shutdown and even safety accidents caused by fault expansion are effectively avoided.

The invention discloses an agent intention understanding method based on multi-modal information fusion, relates to the technical field of natural language processing, and solves the technical problem that multi-agent intention understanding cannot be realized in a dynamic environment. Voice, text and visual features are mapped to the same hidden space through maximum mean value difference minimization, distribution difference between modes is eliminated, joint features in the hidden space can capture cross-mode complementary information at the same time, and the richness and anti-noise capability of feature expression are enhanced. CRF (Conditional Random Field) is combined with a special dictionary in the scheduling field to carry out candidate set expansion on fuzzy keywords, and specific semantic variants in the field are captured. The optimal grammar of the scheduling instruction is analyzed and decomposed into a feature vector causal relationship pair, and components of an intention and logic dependence of the intention are clarified. A dynamic knowledge graph is constructed based on the causal relationship, probability dependence among parameters is quantized by using a Bayesian network, the causal relationship strength is dynamically adjusted, and environmental changes are adapted.

The invention discloses a differential early warning method for a water turbine speed regulating system based on data driving, and aims to solve the problems of poor real-time performance, low accuracy, weak adaptability and lack of differential early warning of a traditional method. The method comprises the following steps: collecting data such as guide vane opening and unit frequency in real time, and preprocessing the data through median filtering and Kalman filtering; identifying working conditions by using a rule engine and fuzzy logic and processing according to the working conditions; extracting time domain and frequency domain characteristic parameters, and constructing statistical indexes; integrating statistics, nonlinearity and twinborn simulation to build a threshold model, and combining a production rule, a fault tree and a Bayesian network to build a rule model; and three types of early warning models are constructed, early warning levels are divided according to an expert scoring method, information is pushed, and a differentiation strategy is formulated. The method improves the early warning real-time performance and accuracy, adapts to complex working conditions, and guarantees the safe operation of the hydropower station.