52 results about "Granger causality" patented technology

The invention discloses an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

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 mainly relates to the technical field of hydrological forecasting. In order to improve the precision and real-time performance of hydrological flow prediction, the invention provides a hydrological flow prediction method based on causal inference and an extended long short-term memory network, and the method comprises the following steps: collecting historical hydrological flow and meteorological factor data, and carrying out the preprocessing; meteorological factors having Granger causality with the hydrological flow are screened out to serve as characteristic meteorological factors for hydrological flow prediction; and establishing a hydrological flow prediction model based on the extended long short-term memory network, taking the historical feature meteorological factors and the hydrological flow data as the input of the hydrological flow prediction model, and training the hydrological flow prediction model to predict the future hydrological flow. The hydrological flow prediction model is more accurate when learning the long-term trend and short-term fluctuation of the hydrological flow, the interpretability and scientificity of a hydrological flow prediction result of the hydrological flow prediction model are enhanced, and a scientific basis and technical support are provided for hydrological management, drainage basin treatment and extreme climate response.

The invention relates to the technical field of port logistics intelligent analysis, in particular to an inter-port congestion propagation inference method based on Granger causal relationship and reserve pool calculation, which comprises the following steps: constructing a multi-directed container ship transportation network according to AIS ship trajectory data based on an L-space modeling method; quantizing the congestion degree of the port by using the average waiting time of the port as a core index; calculating network features of the container ship transportation network; constructing an initial candidate port set; a machine learning prediction model based on the congestion propagation relation between the ports and reservoir calculation is constructed, and the congestion degree of each port in the next time step is predicted; designing a greedy iterative algorithm based on a Granger causality idea, and optimizing a congestion propagation relation inference result of each port based on a congestion prediction error; and constructing a congestion degree prediction model, and taking the congestion propagation relationship as input to realize prediction of the port congestion degree. According to the invention, congestion propagation between ports can be accurately deduced.

The invention relates to a network attack scene intelligent reconstruction and mode evolution analysis method, and belongs to the technical field of network security. The method comprises the following steps: preprocessing multi-source data of a network security event to obtain a low-dimensional feature vector; based on the low-dimensional feature vector, constructing a causal graph in combination with a Granger causal and PC algorithm; on the basis of the causal diagram, detecting an abnormal behavior by using an improved multi-level GAT model, and generating a response strategy; analyzing mode evolution of the low-dimensional feature vectors through dynamic clustering and a space-time attention mechanism; and performing visualization and decision generation based on a mode evolution result and the response strategy. According to the invention, a complete network security detection and defense closed loop can be formed.

The invention relates to the technical field of controller fault diagnosis, in particular to a controller fault root cause distinguishing method and system. A VAR model is adopted to distinguish the fault root cause of the controller, a plurality of mutually associated communication parameters in the controller are modeled into a unified multi-dimensional time sequence system, and modeling and analysis are performed on multivariable time sequence data of the integrated controller in the communication process. A collaborative decision-making mechanism of residual analysis, Granger causal relationship and impulse response function mode comparison is integrated to accurately distinguish whether communication abnormity is originated from an external network problem or an internal processing bottleneck of the integrated controller so as to realize intelligent monitoring of a communication link health state and accurate distinguishing of a fault source. Therefore, the technical problem that the fault root cause of the controller is difficult to accurately position in the prior art is solved.

The application provides a road slope stability prediction method and device, a storage medium and an electronic equipment, which comprises the following steps: collecting multi-dimensional environmental parameters of a road slope in real time to form an original parameter set; analyzing the original parameter set based on a Granger causality test algorithm to determine the causal influence relationship between parameters, construct a parameter causal network, and screen out key parameters; calculating the similarity of each key parameter in the time and space dimensions through a dynamic time warping algorithm to generate a space-time correlation matrix; weighting and fusing the key parameters based on the space-time correlation matrix to obtain a fused parameter; inputting the fused parameter into a pre-trained slope stability prediction model to obtain a predicted value of slope stability; and outputting a slope instability warning information when the predicted value exceeds a dynamically updated warning threshold. In the application, the defects of inaccurate prediction and data redundancy of the current road slope stability prediction method are overcome.

The invention discloses a coastal zone ecological safety early warning method and system based on big data, and relates to the technical field of big data analysis, and the method comprises the steps: determining a coastal type through remote sensing and geological map spots, carrying out the ecological multi-source data collection according to the coastal type, and carrying out the data preprocessing; based on preprocessed data, a sparse attention mechanism is combined with an LSTM network, key ecological factors are dynamically recognized, and ecological index trends are predicted, the time causal relationship between ecological variables is recognized through a Granger causal test method, the root cause traceability of early warning is improved, information granulation modeling is combined, and the early warning efficiency is improved. According to the method, the processing capacity of the system on nonlinear and fuzzy information is enhanced, the stability and adaptability of an early warning model are improved, through conditional transfer entropy analysis, intermediary interference is effectively eliminated, the accuracy of causal identification is ensured, and finally accurate identification, interpretable early warning and scientific intervention on coastal zone ecological safety problems are achieved.

The invention provides a power generation equipment reliability dynamic evaluation method based on a Bayesian network, and belongs to the technical field of power equipment state monitoring. Through joint analysis of mutual information and Granger causality test, a coupling relationship between parameters and a health state evolution rule are accurately captured, and the reliability of power generation equipment evaluation is improved; dynamic self-adjustment of model parameters and structures is realized based on variational Bayesian inference of a sliding window and KL divergence drift detection, and the method can adapt to non-stationary environments such as equipment aging and working condition change. Besides, a forward-backward algorithm is combined with an expert knowledge base, a probabilistic health state and a quantitative reliability index are output, and closed-loop support from component-level fault early warning to system-level maintenance decision making is realized.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention discloses an electric power data driven causal network construction method based on two-stage causal inference, and the method comprises the steps: collecting multiple types of monthly electricity consumption time series data, preliminarily screening candidate association pairs from the monthly electricity consumption time series data through an adaptive joint significance threshold adjustment mechanism, and obtaining a candidate set; dividing a candidate set by adopting a sliding window mechanism, carrying out transfer entropy test and Granger causality test based on a hybrid binning strategy on each candidate association pair in the divided candidate set, calculating a weighted fusion index based on transfer entropy test saliency and Granger causality test saliency, and carrying out fusion judgment to obtain a causality edge set; and generating a causal network based on the causal edge set, performing loop optimization processing on the causal network, and generating a directed acyclic causal network with time delay annotation. According to the method, the problems of stability hypothesis distortion, low search efficiency, lack of an adaptive optimization mechanism and the like in the prior art are solved.

The invention relates to an industrial quality prediction method based on priori knowledge constraint graph convolution, and the method comprises the steps: collecting multivariable time series data containing quality variables and process variables, deeply mining the Granger causality between the variables based on the multivariable time series data, preliminarily obtaining a directed information transfer matrix, and carrying out the deep mining of the Granger causality between the variables; combining a prior sub-process knowledge mask matrix to dynamically adjust and refine an information transfer relationship between variables so as to generate a dynamic adjacency matrix; and designing a multi-head space-time diagram convolution long-short-term memory network based on the dynamic adjacency matrix to learn long-short-term space-time characteristics. According to the method, correlation between a quality variable and a process variable is effectively mined by adopting a Granger causal relationship based on constraint priori knowledge, and a long-short term dependency relationship is captured by utilizing a multi-head space-time diagram convolution long-short term memory network, so that the accuracy of quality prediction of an industrial system is improved.

The application provides a kind of multivariate spatiotemporal data causal cascade mode mining method and system, it is related to data processing technical field.The method comprises: collecting multivariate spatiotemporal data;Multivariate spatiotemporal data is segmented to time lag perception, obtain multiple subsequences, and multiple subsequences are defined as multivariate spatiotemporal event data set;The causal cascade propagation probability between two different events is established, and the causal cascade mode with the causal cascade propagation probability greater than the set threshold is selected;According to all causal cascade modes mined, a dynamic causal cascade network is constructed;From the dynamic causal cascade network, the regularized causal cascade network is screened according to the frequency of causal cascade mode.The application combines the variable time lag granger causality inference model with the multivariate spatiotemporal cascade mode mining method, designs a variable time lag granger causal cascade mode mining algorithm, which can accurately extract the causal cascade mode in multivariate spatiotemporal data.

The invention provides a robot skill knowledge characterization method based on Granger causality test. The method comprises the following steps: collecting original video stream data in a robot operation process; standard operation steps and sudden abnormal conditions of the robot are coded into a standard operation state and a random event state respectively, stage variables are formed, executable operation after the standard operation or the random event occurs is coded into an executable operation state, and action variables are formed; modeling the sequential relationship between the historical stage variable and the current action variable by using a VAR model to obtain a VAR-based stage-action model; performing Granger causality test on variables in the stage-action model based on the VAR to confirm the causality between the variables; and according to the result of the Granger causal test, constructing a causal relationship network in the robot operation process, and forming a causal rule device. According to the method, the real dependency relationship in the time series data can be deeply mined, and a skill characterization framework based on a causal mechanism is established.

This invention relates to the field of electro-digital data processing technology, specifically to a method and system for monitoring the dispersion state of mesoporous carbon electrode slurry based on data fusion. The method includes: performing Granger causality tests on time-series monitoring data to identify target sensor pairs and their causal directions, determining the causal and consequential sensors; performing multi-scale decomposition; performing cross-scale correspondence analysis to determine the cross-scale correspondence layer; setting a prediction window and verifying cross-scale response during real-time monitoring; and calculating the dispersion state index of the mesoporous carbon electrode slurry. This invention establishes a causal correlation and transfer parameter model between monitoring data at the micro-particle scale and macro-flow behavior scale of mesoporous carbon electrode slurry through the coupling of Granger causality tests and multi-scale decomposition, enabling the dispersion state assessment to reflect cross-scale dynamic evolution rather than local information at a single scale.

A system includes: a memory (116) for storing computer-executable components; and a processor (120) operatively coupled to the memory (116) and capable of executing the computer-executable components stored in the memory (116). The computer-executable components may include a maintenance component (108) that can detect causes of mechanical system failures by employing a greedy hill-climbing process to perform a polynomial number of conditional independence tests.

This invention provides a method for injecting industrial knowledge based on retrieval enhancement, belonging to the field of industrial knowledge technology. This invention establishes a time-series data flow matrix by collecting multi-source heterogeneous data, constructs a time-series causal knowledge graph using Granger causality tests, establishes an industrial knowledge document library with a hybrid index structure, performs time-series-aware query expansion on the query input, performs a hybrid retrieval of dense vectors and sparse inverted indexes and cross-encodes and reorders the data, associates the refined documents with the time-series causal knowledge graph to extract event evolution paths and fuses them to generate a time-series enhanced knowledge representation, inputs it into a time-series knowledge enhancement model to generate answer text containing fault analysis and prediction suggestions, injects it into an industrial decision support system after quality assessment, and stores feedback data for continuous model optimization. This solves the technical problem of industrial knowledge retrieval being disconnected from real-time temporal status, resulting in a lack of time-series causal reasoning ability in the generated answers.

The application relates to the technical field of fault prediction, and discloses a water level monitoring equipment fault prediction method based on multi-source data fusion, which comprises the following steps: performing stationary processing on multi-source time series data to obtain weakly stationary multivariate sequences; constructing a dynamic coupling relationship network through a dynamic conditional covariance model and a Granger causality test; extracting a topological feature vector therefrom, establishing a deviation degree reference model based on the topological feature vector of a historical normal period, and calculating the deviation degree of the topological feature vector to obtain an equipment coupling health degree index; and generating a fault prediction warning based on the evolution trend of the index and the matching degree of the index with a pre-stored fault mode; the application can improve the accuracy of a water level monitoring equipment fault prediction based on multi-source data fusion.

The invention provides a social situation factor modeling and intelligent fusion method and system based on time dynamics to solve the problems that in existing network information demand prediction, social situation factors lack time dynamics modeling, news recognition precision is low, a fusion mechanism is static, and multi-source factors do not have a unified framework. According to the method, three types of situation factors are collected in a sampling period of one hour, and classification completion is carried out according to an Ingwersen framework; traversing [24, 24] hours through the CCF to determine the optimal lag time, and extracting the time dynamic characteristics in combination with Granger causality test (plt; using a BiGRU + CRF classifier to identify sensitive news and quantify emotion intensity; and according to factor types, a differential attenuation function is matched to establish a dynamic weight mechanism, and feature vectors are fused and output. The method can be used as an independent module for a time sequence prediction model; in the BAI data set emergency scene, the MSE is reduced by 47.8%, the news identification accuracy is 91.2%, the community scene prediction precision is improved by 32.1%, the operation and maintenance response time is shortened by 57%, and the prediction precision and robustness are effectively improved.

The application relates to an industrial quality prediction method based on prior knowledge constraint graph convolution. The method collects multivariate time series data containing quality variables and process variables, deeply mines the Granger causality relationship between the variables based on the multivariate time series data, preliminarily obtains a directed information transmission matrix, and dynamically adjusts and refines the information transmission relationship between the variables in combination with a prior sub-process knowledge mask matrix to generate a dynamic adjacency matrix. A multi-head spatiotemporal graph convolution long short-term memory network is designed based on the dynamic adjacency matrix to learn long short-term spatiotemporal features. The method uses the Granger causality relationship based on the constraint prior knowledge to effectively mine the correlation between the quality variables and the process variables, and uses the multi-head spatiotemporal graph convolution long short-term memory network to capture long short-term dependency relationships, which helps to improve the accuracy of industrial system quality prediction.

The invention relates to the technical field of after-sales satisfaction evaluation, in particular to an after-sales evaluation method and system for enterprise marketing consultation service, which identifies time lag association between each behavior and score through a Granger causal relationship test method, can establish dynamic mapping of score influence paths in a service process, and improves the evaluation efficiency. A key driving link of score feedback is accurately identified, and multi-cycle behaviors such as plan execution, customer return visit and suggestion revision are matched and rearranged by constructing a unified time coordinate structure, so that the alignment precision between service behavior data and score data is improved, the misjudgment risk caused by score time drift is reduced, and the service quality is improved. By introducing a nonlinear state conversion and periodic aggregation mechanism, a nonlinear change trend existing in a scoring sequence is captured, and error modeling is performed on a scoring evolution process in combination with a long and short-term memory network, so that trend deviation caused by short-term mutation and periodic abnormality can be compensated, and the scoring evolution process is more accurate. And the prediction precision and the scoring behavior explanation capability of dynamic modeling are improved.

The present invention relates to a causal relationship visual analysis method for multivariate time series, belonging to the field of visualization technology, and comprising the following steps: S1: obtaining a multivariate time series data set and preprocessing the time series from the perspectives of space and time respectively; S2: applying the Granger causality test to perform a causal relationship test on the divided multivariate time series, and using an anti-shake strategy to obtain a stable causal relationship; S3: designing a dynamic causal graph visualization to enable analysts to explore and interpret the dynamic causal relationship of the multivariate time series along time; S4: considering multiple dimensions of causality, designing customized causal verification and comparison visualization to reveal suspicious causal relationships.

The application aims to provide a scalp EEG-based epilepsy lesion positioning system, which is based on Granger causality theory, establishes brain function networks of patients in interictal period and ictal period respectively, comprehensively uses effective information in different periods, analyzes the connectivity difference between the initial stage of seizure and the interictal period from the network level, and analyzes the connectivity difference between the lesion area and the non-lesion area from the node level; adopts a difference quantification method to quantify the difference size of the causal flow of each lead between the interictal period and the initial stage of seizure, and sequentially identifies the seizure main frequency band, the seizure side and the seizure lead based on the difference size, so as to realize the lesion positioning and side identification of epilepsy patients.

The application relates to the technical field of data processing, and discloses a computing power server system layer optimization method and system for a high-load scene. The method comprises the following steps: collecting micro-performance indexes such as CPU instruction cycle numbers and page table missing times, constructing a three-layer causal directed acyclic graph through Granger causality test, reducing a parameter search space based on a bottleneck node reverse backtracking, and generating an interpretable optimization decision with a causal path and contribution quantification, so as to solve the problems that an existing technology cannot accurately locate a performance bottleneck root cause and an optimization result lacks transparency. The application performs bottleneck node reverse backtracking and parameter space pruning based on a causal graph, and solves the problems that the existing technology cannot accurately locate the performance bottleneck root cause and blind exploration of the parameter space leads to low optimization efficiency.

The application discloses a kind of production and operation data early warning method and system, it is related to industrial energy management technical field, method includes: constructing energy flow directed graph;Extracting space-time embedding vector and attention weight matrix by graph attention network;Perform granger causality test, construct energy causal conduction atlas;Characteristic fusion is carried out to space-time embedding vector, attention weight matrix and causal conduction atlas, and abnormal score is obtained by inputting abnormality detection model;Reverse causal link traversal is carried out to abnormal node, and root cause node is positioned;Early warning information with common root cause node is associated and aggregated, and aggregated early warning event is output;Matching disposal suggestion and being associated to aggregated early warning event output;The coupling relationship of multi-medium energy is modeled by graph attention network, and root cause positioning is realized by granger causality test to construct causal conduction atlas, alarm storm is avoided by alarm aggregation, and the accuracy of energy consumption early warning in production and operation data of cigarette factory and operation and maintenance efficiency are improved.

This invention belongs to the field of industrial big data analysis technology, and provides a real-time early warning system and method for a manufacturing order status monitoring big data platform. This method dynamically generates a list of characteristic requirements and proactively discovers new risk patterns, guiding the adaptive fusion of multi-source data to generate a preliminary feature map. Based on this map, features are calculated in real time and the order delivery probability is predicted, generating vectors with contribution labels. The system uses dynamic thresholds to trigger early warnings and forms a problem hypothesis package by matching historical patterns through a feature semantic network. Furthermore, candidate root causes are located in the production knowledge graph, time-series data is extracted and verified through Granger causality tests, and finally, the feature evolution path of the risk is constructed and output. This invention achieves full-process automation from intelligent perception, prediction, root cause tracing to closed-loop optimization, significantly improving the accuracy, interpretability, and self-evolutionary capability of early warnings.

The application provides a kind of based on edge computing's downhole equipment fault real-time diagnosis method and system, it is related to coal mine safety production technical field, including: through distributed sensing network acquisition multimodal data and extract multiscale time series feature, feature is projected to Lie group manifold space and constructs coupling mapping relationship matrix to obtain fusion characteristics, based on topological connection relationship and granger causality coefficient constructs causal directed acyclic graph, executes bayesian probability inference and combines entropy value similarity matching to determine execution strategy, carries out deep time-frequency analysis and causal chain verification.Realize the high-precision real-time diagnosis of equipment fault in the complex environment of downhole, improve the fault early warning accuracy.

The application discloses a blast furnace abnormal furnace condition root cause analysis method fusing expert knowledge and Granger causality, which comprises the following steps: integrating furnace condition perception priori knowledge, determining a key variable set according to a current abnormal furnace condition type, and ensuring that the root cause analysis can capture a key target link; based on the Granger causality thought, learning a causality matrix among variables in a variable prediction process, and adopting an adaptive regularization strategy to dynamically adjust the causality matrix; in a time lag process among the variables, firstly performing fast positioning in a coarse granularity, then performing fine search, and further performing global optimization by means of a particle swarm optimization algorithm, and finally adjusting part of the time lag relations in combination with physical priori; constructing a link search and confidence evaluation process, performing propagation path search under the constraints of physical grouping priori, a causality matrix and a time lag matrix, and combining information such as process rules to construct a comprehensive link scoring index, and the application well makes up for the deficiencies of traditional methods in the aspect of blast furnace abnormal furnace condition root cause analysis.

The invention belongs to the technical field of disturbance source identification of an electric power system, and particularly discloses a dominant disturbance source identification method and system based on causal inference, and the method comprises the steps: collecting disturbance characteristic time sequence data of a plurality of monitoring nodes in the electric power system, and constructing a two-dimensional vector autoregression model of any two disturbance sources according to the disturbance characteristic time sequence data; the residual sum of squares of the two-dimensional vector autoregression model is calculated based on Granger causality test, the multi-disturbance source causality is judged, and then a causality coupling influence matrix is constructed; and based on the causal coupling influence matrix, calculating the influence degree, centrality and cause degree, drawing a causal coupling relation graph, determining the causal coupling strength and the causal coupling direction between the disturbance sources, and realizing dominant disturbance source identification. The method solves the problems that an existing disturbance source identification method is difficult to reveal the transmission relation between disturbances, cannot describe the coupling process between multiple disturbance sources, and lacks the identification capability on a dominant disturbance source.