78 results about "Causal link" patented technology

The invention relates to the technical field of causal reasoning, and provides a method and device for constructing a multi-dimensional causal event knowledge graph. The method comprises the following steps: extracting event nodes, relation edges and various characteristics of the event nodes from data; calculating interaction strength among different event nodes, and performing causal relationship reasoning among the different event nodes according to the interaction strength; calculating the feature coupling strength between different features, and analyzing the development law of each feature coupling strength; and performing feature causal evolution reasoning of the event nodes according to the development law of the coupling strength of each feature. According to the method, the characteristics of multiple dimensions are extracted, and the interaction strength and the characteristic coupling strength are calculated by using the multiple dimensions, so that the quantification of the causal relationship is realized, and the causal relationship in the causal event is completely captured by performing causal reasoning from the two dimensions of the event node and the characteristics.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention relates to the technical field of aircraft fault diagnosis, in particular to a vertical take-off and landing aircraft diagnosis generalization enhancement method based on causal weight. Firstly, a directed acyclic graph between flight features and fault types is established through an information flow causal discovery method, the causal relationship between the flight features and the fault types can be clearly presented, and a basis is provided for subsequent analysis. Then, mutual information between nodes is calculated and converted into causal strength for causal enhancement, and expression of the causal relationship in the data is enhanced; in the convolutional neural network, causal weights are used for dynamic weighting to extract local features, causal features of a directed acyclic graph are combined, and feature information of different types is fully utilized. According to the method, features are fused through a graph convolutional network to obtain high-order features, adversarial training is completed through a full-connection network, the constructed fault prediction model integrates various technical advantages, the generalization ability of the model is effectively improved, the model can better adapt to different working conditions and data changes, and aircraft faults are accurately diagnosed.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention provides a maintenance scheme matching and recommendation method based on causal and equipment structure enhancement, and belongs to the field of equipment maintenance, and the method comprises the steps: S1, constructing a maintenance field synonym library; an unsupervised SimCSE model is trained, and a standardized knowledge graph IDS-EMKG-QA is obtained; optimizing the question sentences to obtain normalized question sentences; s2, template-based intention recognition is performed on the standardized question, if the question is in an intention recognition template, an answer is generated and matched based on the query statement, and if the question is not in the intention recognition template, the step S3 is executed; s3, reasoning and screening out candidate answers in IDS-EMKG-QA based on a fault causal link and an equipment composition architecture according to the normalized question by using a graph search mode; and matching and sorting the candidate answers and the normalized question sentences by adopting a RotatE graph embedding model to obtain a recommendation result. The method solves the problems of diversity and complexity in the maintenance process of complex equipment.

The invention discloses an accident scene reasoning method and device. The method comprises the following steps: extracting a causal relationship mention from historical data and constructing a causal relationship mention data set; the historical data records cause events and result events of historical accidents; the causal relationship mention represents a text containing a causal relationship pair; the causal relationship pair comprises a cause event and a result event which are associated with each other; analyzing the causal relationship mentions in the causal relationship mention data set into causal events; based on the causal events, integrating at least two common causal events to form a generalized causal event network; and reasoning an accident to be analyzed based on the generalization causal event network. According to the accident scene reasoning method constructed by the invention, valuable causal knowledge can be mined from a large number of English accident reports, and a useful tool capable of predicting future accidents can be formed.

The invention relates to an unsupervised construction method and system for a railway operation accident-oriented affair graph, and the method comprises the following steps: obtaining railway operation accident report corpus data, and carrying out the preprocessing to obtain a railway accident report data set; based on the railway accident report data set, classifying all accident report information into a plurality of subsystems according to cause codes of accidents, and extracting keywords of risk events corresponding to cause types as risk event entities; through layer-by-layer decomposition of a subsystem, identifying a causal relationship and a sequential relationship between risk events, and establishing an event relationship to determine a matrix; and determining a matrix based on the risk event entity and the event relationship, calculating the similarity between the to-be-linked railway operation accident report and the risk event entity, selecting and complementing an optimal accident risk chain, and constructing a affair graph model. Compared with the prior art, the method has the advantages of realizing unsupervised construction of the railway operation accident affair atlas and the like.

The invention provides a causal relationship and time sequence characteristic-considered cascading failure rapid screening method and system, and relates to the technical field of power grid cascading failures, and the method comprises the steps of obtaining cascading failure scene data; based on cascading failure scene data, extracting a causal relationship between an initial short-circuit fault and a subsequent fault by using logistic regression and a neural network model; different states of the fault event are converted into graph structures, time delays between the different states of the fault event are obtained based on cascading fault scene data statistics, and time sequence features in the transient evolution process of the cascading fault event are extracted; the probability that the initial fault causes a subsequent fault event is obtained based on a causal relationship, the duration of different states of the fault event is generated by adopting a Monte Carlo sampling method based on time sequence characteristics, and a cascading fault scene is generated; and carrying out risk assessment and probability assessment on the cascading failure scene generated based on the causal relationship and the time sequence characteristics, and finally screening to obtain a high-risk cascading failure scene.

The present disclosure provides a method and system for quickly screening cascading failures that consider causal relationships and timing characteristics, and relates to the technical field of cascading failures in power grids. The method comprises the following steps: obtaining cascading failure scenario data; extracting the causal relationship between an initial short-circuit fault and subsequent faults using logistic regression and a neural network model based on the cascading failure scenario data; converting different states of a fault event into a graph structure, and obtaining time delays between different states of the fault event based on statistics of the cascading failure scenario data, and extracting timing characteristics during the transient evolution of the cascading failure event; obtaining the probability of an initial fault triggering a subsequent fault event based on the causal relationship, and generating a cascading failure scenario using a Monte Carlo sampling method based on the timing characteristics; and performing risk assessment and probability assessment on the cascading failure scenarios generated based on the causal relationship and timing characteristics, and ultimately screening out high-risk cascading failure scenarios.

The invention discloses a fault root cause tracing method based on a dynamic time sequence causal network. The method comprises the following steps: performing sliding window extraction on time sequence data of a normal mode and a fault mode to obtain sub-time sequences; calculating a causal intensity fluctuation range # imgabs0 # of each edge by using a causal network constructed by a sub-time sequence of a normal mode; for each causal network constructed by the sub-time sequence of the fault mode, finding out an abnormal causal relationship in the network by judging whether the causal strength of each edge exceeds an allowable fluctuation range # imgabs1 #, and calculating to obtain reliability distribution of a fault root cause; and finally, carrying out fusion processing on all the obtained reliability distributions of the fault root causes to obtain final reliability distributions of the fault root causes, and determining the fault root causes according to the distribution. The method can more accurately capture the dynamic change of the causal relationship between the variables, and achieves the accurate positioning of the fault root cause.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The application provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, which comprises the following steps: constructing an accident chain knowledge graph and a physical trigger graph, establishing the causal relationship and physical constraints among equipment, state, event and consequence; mapping the historical records, expert rules and online observation data into graph entities and relationships to form a baseline accident scene and calculate baseline indicators; generating candidate paths by using graph reasoning and coupled multi-physical field simulation, and realizing the closed loop of graph reasoning and physical calculation through consistency checking; scoring and perturbation simulation analysis on the candidate paths, screening the robust target path; carrying out high-fidelity simulation on the target path, identifying key nodes combined with sensitivity analysis and minimum cut set, and generating disposal suggestions and action priority. The application realizes high credible prediction of accident evolution and closed loop linkage of emergency response, and has high precision, high robustness and engineering implementability.

The invention discloses a controllable flight ground collision risk judgment method and system based on a Bayesian network model, and relates to the technical field of controllable flight ground collision risk judgment. And carrying out classification identification on causes of controllable flight ground collision (CFIT) unsafe events. The complex and disordered cause factors are sorted through an interpretation structure model (ISM), and a hierarchical relation schematic diagram capable of clearly showing the mutual causal relation and hierarchy of the cause factors is obtained. A Bayesian network model is established, and key factors in a CFIT event are sorted out from three different angles. According to the analysis result, various external factors and unit capability defects have influences on the CFIT, and the last defensive line for judging key links and preventing and controlling CFIT events can be found. More detailed guidance and training are provided for the key links, and more standardized supervision is carried out.

The application provides an enterprise resource planning system data flow conversion monitoring method, relates to the technical field of abnormal cause analysis in an enterprise resource planning system, and aims to solve problems such as difficult extraction of abnormal propagation causality, unexplainable traceability path, and low efficiency of quasi-real-time cause analysis in a multi-node complex business process. The core technical scheme includes the following steps: collecting and standardizing time series data of business key state indicators, establishing a local causal correlation cache by using a sliding window and a causal reasoning algorithm, recursively tracing back to an abnormal node as a terminal to form a preliminary causal path, screening high-confidence causal links through multi-dimensional time consistency evaluation, generating a simplified dynamic path by using a node semantic aggregation mechanism, and finally embedding the path in an operation and maintenance interface in the form of a visual trajectory to realize interactive cause analysis verification. The scheme improves the accuracy and explainability of abnormal cause analysis, realizes full-process automation and man-machine fusion optimization from data to root cause links, and significantly enhances the intelligent level of system abnormality handling.

To comprehensively derive the causal relationships between multiple factors. [Solution] The processing device 100 classifies a plurality of causal exploration factors, which include an influencing factor and influencing factors that may influence the influencing factor, into one of a plurality of hierarchy levels according to the causal order of the plurality of causal exploration factors, and derives the causal relationships of the causal exploration factors belonging to multiple hierarchy levels that are different from each other.

The invention discloses a highway bridge engineering construction safety monitoring method. The highway bridge engineering construction safety monitoring method comprises the steps of obtaining highway bridge construction historical accident data; extracting structured information from the accident report text to determine an accident label and risk factors associated with the accident label; processing co-occurrence data of the risk factors in the accident case by using a preset association rule mining algorithm, and generating a risk factor association rule set representing a potential causal relationship among the risk factors according to the co-occurrence data; constructing a probability graph model of the risk factors based on causal semantics of the risk factor association rule set, wherein the probability graph model represents a dependency relationship among the risk factors; based on the probability graph model, determining sensitivity values of at least part of risk factors by using a preset importance degree measurement algorithm; determining a key risk factor according to the sensitivity value of the risk factor and a preset sensitivity threshold; and determining safety monitoring indexes of highway bridge engineering construction according to the key risk factors.

The application discloses a cross-region synchronous power monitoring system and method based on a time series database, and relates to the technical field of data analysis.The method comprises the following steps: acquiring historical abnormal events and storing them into a time series database, extracting first and second event features therefrom, calculating a perception response duration and a recovery treatment duration, and taking the two as third event features; step-by-step construction of corresponding event sequence models to obtain a complete event sequence model set of all abnormal events; screening of event pairs by traversing the set, analysis of causality based on the event pairs, determination of a causal relationship, and generation of a causal link pair set; traversal of the causal link pair set, marking of associated causal chain pairs and grouping; establishment of a mapping and storage into a knowledge base, marking of consequence types and average recovery durations of each situation element; real-time monitoring of situation element abnormalities, query of the knowledge base to find common consequences, calculation of an estimated maximum recovery time, and early warning if the estimated maximum recovery time exceeds a threshold value.The application realizes cross-region synchronous power monitoring.

To improve the accuracy of root cause analysis.SOLUTION: A root cause analysis system 1 that recommends a root cause of an abnormality occurring at an analysis target based on normal data and abnormal data obtained by measuring the analysis target with a plurality of sensors includes: a causal relationship information processing part 351, a significance level calculation part 352, and a recommendation result calculation part 3531. The causal relationship information processing part, based on the normal data, calculates a causal structure that links a plurality of sensors through causal relationships and an influence level of each of the plurality of sensors. The significance level calculation part, based on the normal data and the abnormal data, calculates the significance level of each of the plurality of sensors. The recommendation result calculation part, based on the causal structure and the significance level, extracts a plurality of recommended sensors as a target of recommendation, and determines a recommendation ranking of each of the plurality of recommended sensors based on the causal structure and the influence level to calculate recommendation results in which the plurality of recommended sensors are arrayed in order of the recommendation ranking.SELECTED DRAWING: Figure 1

The invention relates to the technical field of mechanical fault diagnosis, and discloses a small sample fault diagnosis method based on a causal relationship and a terminal. According to the method, a fault diagnosis model of a causal small sample relation measurement network is constructed, and the fault diagnosis model is mainly composed of a feature coding module, a causal intervention module, a causal decomposition module and a relation measurement module. In the causal intervention module and the causal decomposition module, causal factors are extracted from input, then factorization loss is designed to reconstruct an invariant causal mechanism, the invariant causal mechanism is converted into independent features with a sufficient causal relationship, and finally a feature coding module and a relationship measurement module train a plurality of meta-tasks to obtain a plurality of meta-tasks. And a trainable similarity measurement space is established to learn the feature distance between the sample pairs. Therefore, the model can capture the causal characteristics of the distribution differences among the components, the interpretability and generalization ability of the model are remarkably improved, and the accuracy of fault diagnosis is ensured.

The invention provides a power grid accident automatic submission method and system based on AI semantic understanding and event chain tracking, and relates to the technical field of intelligent power grid monitoring. The method comprises the following steps: constructing an event chain model through event time alignment and equipment topological association, and deducing a causal relationship between events to generate an electrical law causal graph; identifying a root cause event in combination with the in-degree, out-degree and confidence weight of the node; and automatically judging and generating an accident submission text and an evidence packet according to the accident severity and the confidence threshold. According to the invention, intelligent diagnosis and automatic submission of electrical accidents can be realized, and the analysis accuracy and response efficiency are improved. The problem that in the prior art, cross-system time alignment and automatic power grid accident submission based on causal constraints cannot be achieved under the multi-source heterogeneous data condition is solved.

The present invention discloses a system and method for identifying unsafe factors in the process of new energy infrastructure construction. The specific technical solution is as follows: constructing a feature database of unsafe factors in different construction stages and different accident types, calculating the similarity between the unsafe factor feature vectors collected in real time and the unsafe factor feature vectors in the feature database, and when the calculated similarity exceeds a set similarity threshold, preliminarily identifying the current unsafe factor, and then analyzing the causal relationship, temporal sequence, and spatial relationship between the current unsafe factor and other unsafe factors associated with it, constructing a factor association network model, determining the unsafe factor chain, and mining potential unsafe factors. By analyzing the mutual influence between the current unsafe factor and the potential unsafe factor and deeply mining the complex relationship between the data, it can help staff fully understand the root causes and development trends of unsafe factors and formulate more effective response measures.

The present invention proposes a maintenance solution matching and recommendation method based on causality and equipment structure enhancement, which belongs to the field of equipment maintenance, including: S1: constructing a synonym library in the maintenance field; training an unsupervised SimCSE model to obtain a standardized knowledge graph IDS-EMKG-QA; optimizing the question sentence to obtain a standardized question sentence; S2: performing template-based intent recognition on the standardized question sentence, if the question is in the intent recognition template, then generating and matching the answer based on the query statement, if not, go to step S3; S3: using a graph search method, according to the standardized question sentence, inferring and screening candidate answers based on the fault causal link and the equipment composition architecture in IDS-EMKG-QA; using the RotatE graph embedding model to match and sort the candidate answers and the standardized question sentence to obtain the recommendation result. The method of the present invention solves the diversity and complexity problems in the maintenance process of complex equipment.

A causal device, which includes a causal module and a causal feature learning module coupled to the causal module, and a causal method thereof is disclosed to ensure accurate fusion of hybrid imaging or improve priority triage of imaging tests. The causal module is configured to identify or utilize causal relationship(s) between a plurality of variables; the causal feature learning module is configured to extract at least one first causal feature of one of the plurality of variables.

To make it possible to efficiently identify causes of an incident.SOLUTION: An incident event cause identifying system includes: a device log information holding part 32 holding log information on an object device; a causal relation graph holding part 31 holding a causal relation graph associating an incident event with its causal relation; an incident detection processing part detecting an incident in the object device; a necessary log information determination processing part 23 which extracts a causal relation graph of a corresponding type with respect to the incident event detected by the incident detection processing part, and determines device log information necessary for identifying a cause on the basis of the extracted causal relation graph; a necessary log information collection processing part 24 collecting device log information determined by the necessary log information determination processing part 23; and a cause analysis processing part 25 identifying a cause of the incident event by using the device log information collected by the necessary log information collection processing part 24 and the causal relation graph.SELECTED DRAWING: Figure 2

The application relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system, wherein the current transformer error dynamic monitoring method comprises the following steps: obtaining current transformer time sequence data and system event logs; constructing a time sequence causal diagram to represent the time correlation between events and error changes; applying a counter causal model to identify potential causal links; designing a multiple world simulation engine to generate counterfactual scenarios; quantifying causal effects by comparing actual observations with counterfactual simulation results; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal explanation report and adjusting a compensation strategy; the application breaks through the limitations of traditional correlation analysis, can accurately distinguish between causality and correlation, accurately identifies the real trigger factors of current transformer error changes, reduces the false positive rate and the false negative rate, and realizes accurate dynamic monitoring of current transformer errors.

The present application discloses a graph construction method and related equipment, the method comprising: after obtaining a text to be used including at least one group of causal events, first extracting at least one cause event to be used from the text to be used; then determining the result event corresponding to each cause event to be used according to each cause event to be used and the text to be used; finally, determining a causal event graph according to at least one cause event to be used and the result event corresponding to the at least one cause event to be used, so that the causal event graph is used to record the causal relationship existing in the text to be used, thereby achieving the purpose of automatically constructing a causal event graph, thereby reducing the construction cost of the causal event graph.

The application relates to the field of artificial intelligence models and discloses a method, system, device and storage medium for evaluating content causality of a generative engine based on a structured causal model. The method comprises the following steps: obtaining a structured causal graph of a target industry field, user query information and to-be-evaluated content information; inputting the user query information and the to-be-evaluated content information into a causal enhancement model, performing causal link analysis on an original answer after the original answer is generated, extracting causal inference information and mapping the causal inference information to nodes of the structured causal graph, generating causal path data, quantifying causal necessity of the to-be-evaluated content information, obtaining a causal necessity index, a path causal contribution degree and a counterfactual influence index of the to-be-evaluated content information, and then outputting an evaluation result of the to-be-evaluated content information. The method provided in the application embodiment improves the stability of a model, thereby reducing the probability of redundant information in a model output result.

The application discloses a kind of based on causal event extraction model's causal event extraction method, comprising the following steps: based on event extraction and causal relationship identification two subtasks, construct causal event extraction model;Acquire input sentence;Based on pre-training language model, input sentence is encoded, based on sequence labeling decoder, the coded input sentence is decoded, and the event of existence causal correlation is extracted;Based on causal event extraction model, construct initial background graph;Event is inserted into initial background graph, and updated background graph is obtained;Using graph neural network, the representation of extracted event is obtained by encoding and updating to updated background graph;Based on classifier and the representation of extracted event, the causal relationship between events is obtained, realizes the extraction of causal event.The experimental results of the sentence of different causal event pairs prove the ability of the method of the application to extract complex causal relationship.