41 results about "Granger causality" patented technology

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 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 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 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 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 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.

The application belongs to the technical field of power system artificial intelligence and knowledge graph construction, and discloses a power system graph relationship completion method and system based on multi-source evidence and causal reasoning, which comprises the following steps: obtaining multi-source heterogeneous operation data to construct a multi-source heterogeneous evidence graph; extracting the static semantics and dynamic operation characteristics of entities based on a double-flow encoder, and obtaining the joint representation of the entities through cross-modal attention mechanism fusion; calculating the Granger causality index to generate a causal prior mask matrix, and injecting a graph attention network to aggregate the causal constraints and reason the dynamic relationship probability; using D-S evidence theory to synthesize the rules and fuse the multi-source prediction probability, and outputting the confidence interval to filter the noise relationship; and updating the original knowledge graph by using the high-confidence triplets. The application can mine the implicit linkage relationship between devices, block the information propagation that violates the causality law, quantify the uncertainty of the completion result, and improve the ability and reliability of the graph completion under dynamic conditions.

The invention relates to the technical field of ICU return risk prediction, in particular to an ICU return risk prediction method based on patient state perception. According to the method, a patient state time sequence matrix is constructed through multi-source heterogeneous data collection, and risk scores and a key factor set are generated based on feature extraction and integrated learning; constructing a dynamic risk association graph by adopting a two-stage association analysis method of Pearson correlation analysis and Granger causality test, and revealing a causality and a time sequence relationship among risk factors; performing topological optimization and path weight calculation on the association graph to identify a high-risk evolution path; and finally, integrating the risk score, the association map and the evolution path to generate a visual decision support report. According to the method, the limitation that only isolated risk factors can be provided in a traditional method is broken through, the dynamic association and conduction path of the risk factors can be clearly displayed, a complete basis is provided for a clinician to formulate a targeted intervention scheme, and the accuracy and safety of ICU patient transfer decision making are remarkably improved.

The application discloses a method and system for unknown network attack detection based on a dynamic causal diagram, comprising the following steps: step one: collecting and preprocessing original multi-dimensional data sources to obtain a multi-dimensional feature data set; step two: using a Granger causality test method to construct a preliminary causal relationship diagram; step three: inputting the preliminary causal relationship diagram into an improved Graphormer model to perform causal reasoning; step four: dynamically adjusting the edge weight and node connection relationship in the causal reasoning relationship diagram; step five: generating potential attack features through a generative adversarial network, and identifying normal behavior and abnormal behavior by using a discriminator; step six: performing feature selection by using a CART decision tree to obtain an optimal attack detection feature set; and step seven: performing anomaly detection on the optimal attack detection feature set. Through the improved Graphormer model, the application realizes accurate detection and real-time response of unknown network attacks.

The invention provides a time sequence missing data interpolation method based on Granger causality, and belongs to the technical field of data science and artificial intelligence. Aiming at the problems that an existing interpolation method is difficult to capture time sequence dynamic causal dependence and is poor in feature selection subjectivity and interpretability, a technical scheme of cooperative work of a dynamic causal condition diffusion interpolation model and a neural Granger causal discovery prediction model is constructed in combination with a Granger causal analysis and diffusion generation model. Dynamic feature soft dimension reduction is realized through causal probability graph sampling, and accurate missing data interpolation is completed through forward noise addition and reverse noise reduction. According to the method, the interpolation precision of the high-dimension and high-missing-rate time series data is remarkably improved, feature selection is objective, results can be explained, the method is suitable for multiple fields of medical treatment, finance, meteorology and the like, and robust support is provided for data preprocessing and trend prediction.

The invention relates to the technical field of bioinformatics, in particular to a method for constructing a gene network based on two-stage Granger causality and application, and the method comprises the following steps: firstly, carrying out global Granger causality test based on gene expression time sequence data, and constructing an initial candidate network containing all potential regulation and control relationships; and then, refining the candidate network by adopting a dynamic pruning strategy based on event driving. And before the target gene expression has significant fluctuation, detecting whether the candidate regulatory gene has synchronous significant expression change or not, and verifying the causal driving force of the candidate regulatory gene. And only the regulation and control relationship passing the verification is reserved, so that wrong causal association is effectively removed, and optimization of the network structure is realized. The network constructed by the invention better conforms to a real biological regulation rule, and can better reflect the dynamics and directivity of a regulation relationship in a biological system; the method is especially suitable for the research fields of plant stress response, development regulation and control, system biology and the like, and has wide application prospects and practical values.

The application relates to the technical field of intelligent monitoring, and discloses a kind of intelligent online monitoring method and system for unmanned fire power water sampling, which belongs to the technical field of intelligent monitoring.The method comprises the following steps: obtaining real-time measurement data and performing virtual soft measurement to obtain a theoretical value, calculating the confidence weight of each instrument according to the deviation between the measured value and the theoretical value, constructing a time series causal directed graph through Granger causality test and extracting abnormal response characteristics for weighted fusion, constructing an abnormal propagation path along the causal graph and calculating a confidence score to locate the abnormal root node.The application improves the accuracy of abnormal detection and the reliability of root location of fire power water sampling in an unmanned scenario.

The application discloses a kind of method and system for evaluating the process of drought dynamic propagation in drainage basin, including obtaining drought data and collating, abnormal value detection is carried out to drought data;Multiple types of drought index are calculated, and multi-scale calculation is carried out;Drought event is identified based on three-dimensional connectivity;Drought characteristic parameter is calculated to quantify the effective drought event identified, while the migration track of drought event is quantified, the space-time evolution characteristics of drought event are obtained;According to the time series of multiple types of drought index, Granger causality test is carried out;Drought propagation rate between any two types of droughts is calculated;The correlation of two two drought types under multi-scale is calculated, the drought propagation time is quantified, and the key season and spatial distribution sign of the propagation between droughts are identified.The application realizes the accurate identification and quantitative characterization of the propagation process, response time and spatial difference of basin drought by tracking the dynamic track of drought event and analyzing the interaction between multiple types of drought.

This invention discloses an intelligent identification method for systemic financial risk based on causal networks and multi-model optimization. The method first reads and cleans financial market and corporate financial data to construct a quarterly logarithmic return series. Then, it calculates individual risk exposure based on the CoVaR mechanism and generates binary labels for risk states using a Gaussian mixture model (GMM). Further, it employs Granger causality testing to construct a risk transmission map between enterprises, uncovering structural risk relationships within the system. Next, it constructs a Long Short-Term Memory (LSTM) network and a Gradient Boosting Tree (XGBoost) model to predict risk states over multiple periods, and uses a Particle Swarm Optimization (PSO) algorithm to jointly fine-tune hyperparameters. Finally, it integrates the multi-model prediction results to output a systemic risk score and supports model interpretation and visualization based on SHAP values. This method is applicable to various scenarios such as financial supervision, bank risk control, and asset management, enabling efficient identification and accurate prediction of systemic risks.