73 results about "Causal effect" patented technology

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 discloses a management decision-making method and system based on a knowledge base construction technology. The method comprises the following steps: performing sequential relationship extraction on multi-source financial data to obtain a sequential relationship set related to query content; constructing an event-entity incidence matrix corresponding to the time sequence relation set; according to the time sequence relation set and the event-entity incidence matrix, constructing a dynamic knowledge graph; determining causal effect parameters in the causal graph structure by adopting a dual machine learning model; constructing a structural causal model according to the causal graph structure and the causal effect parameters; and generating an anti-fact prediction result by using the structural causal model, and generating a decision scheme corresponding to the query content based on the anti-fact prediction result. The technical problem that decision information including accurate causal basis and prospective simulation information cannot be generated due to the fact that the causal relationship between financial data is difficult to determine and the intervention effect cannot be dynamically deduced in a related management decision method is solved.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

A government official document fair competition review method and system based on causal inference relates to the technical field of data processing, and comprises the following steps: policy text analysis and causal element extraction, causal graph construction, causal effect estimation, anti-fact simulation and index evaluation, and review conclusion generation. According to the fair competition examination method and system for the government official documents, through a method of combining natural language processing, a causal graph model, quantitative effect estimation and anti-fact simulation, automatic, quantitative and explainable intelligent examination on the fair competition risk of the government official documents is realized, and the potential market effect of policy modification can be predicted.

A fraud call recognition method and system based on causal graph and agent collaboration comprises the steps of obtaining a fraud ASR text, mapping extracted multi-category to-be-mapped objects into semantic concept variable vectors, then learning a causal structure based on a data matrix formed by all semantic concept variable vectors, constructing a fraud causal graph, and recognizing a fraud call in the fraud ASR text. Calculating a causal effect of the reason variable on the result variable, and storing the causal effect as a relation weight in a causal graph; the method comprises the steps of obtaining a to-be-recognized ASR text, mapping multiple categories of to-be-mapped objects extracted from the to-be-recognized ASR text into semantic concept variable vectors, then retrieving matched causal chains in a causal graph to obtain a plurality of candidate causal chains, and then generating fraud judgment and risk degree evaluation values based on a large language model inference engine. And determining whether the to-be-identified call is a fraud call. The invention relates to the field of telecommunication anti-fraud, can deeply fuse a causal knowledge base and a large language model, and effectively improves the effectiveness, adaptability and reliability of communication anti-fraud identification.

The invention discloses a data identification method and system based on data high-dimensional feature deconstruction. The method comprises the following steps: decoupling multi-modal time series data into independent implicit factors with clear physical meanings through a domain knowledge constrained depth generation model; based on the implicit factor sequence, using time sequence causal discovery and anti-fact intervention to construct a causal graph and generate a causal effect vector; constructing a differentiable identification strategy network taking the implicit factor and the causal effect as input, and optimizing system meta parameters through a meta learner according to feedback; robustness loss feedback optimization is generated through causal consistency verification and abnormal injection, meanwhile, a structured identification graph is output, and an incremental model library is established. The system correspondingly comprises four function modules. According to the method, the problems that the features cannot be explained, the causal mechanism is missing and the model adaptability is insufficient are solved, and intelligent data identification which can be explained and is robust and has the sustainable evolution capacity is achieved.

The invention discloses a disease cause identification method based on causal learning. The method comprises the following steps: preprocessing an observable medical data set; calculating a fitting relationship between each to-be-inspected variable and the data set by using a Bayesian marginal likelihood probability, and separating causal and confounding factors in the disease influence factors; an iterative causal relationship generation algorithm is utilized, the fitting degree of the causal relationship is iteratively judged on the basis of a Bayesian scoring criterion, and causal influence factors of disease outcome are described in a causal graph form into a direct level and an indirect level; for the discovered causal relationship, a conditional expectation expression is modeled based on a back door criterion, a front door criterion and a tool variable criterion, and a causal effect value is estimated based on a linear regression model, so that the influence degree of the cause of the disease on the outcome of the disease is quantified. According to the method, a causal learning method is used for researching high-risk influence factors possibly causing diseases, the causal effect value and the change trend of the direct influence factors and the target result are determined, and the cause of related diseases can be known.

The invention relates to the technical field of computers, and provides a causal relationship analysis method and device, equipment and a medium, and the method comprises the steps: generating an initial causal network structure according to an association relationship between variables in observation data; based on the number of shared adjacent nodes of the variable nodes in the initial causal network structure in the domain knowledge graph and the path length between the nodes, determining the semantic association degree between the variable nodes; according to the semantic association degree, adjusting the confidence degree of causal edges in the initial causal network structure, and generating a target causal graph; and calculating causal effect intensity among the variable nodes based on the target causal graph, and generating a causal analysis result by using the causal effect intensity. According to the method, the deviation of the initial causal network structure is corrected by utilizing a mechanism of fusing the observation data and the knowledge graph, and the causal effect intensity is accurately calculated based on the target causal graph, so that a causal analysis result containing an accurate causal relationship and a quantitative influence degree can be provided for a user.

The present invention makes it possible to estimate an integrated causal relationship between variables that span a plurality of datasets, and to estimate individual causal effects in each dataset.　A processing unit (12) acquires a plurality of pieces of first data, each of which includes a plurality of variable values that correspond to a plurality of variables, and which are used in estimating causal relationships and causal effects between variables based on the plurality of variable values. The processing unit (12) sets first information relating to an integrated causal relationship and causal effect in the entirety of the plurality of pieces of first data, and second information relating to weighting of the causal effect included in the first information for each of the plurality of pieces of first data. The processing unit (12) generates a plurality of pieces of second data that correspond to the plurality of pieces of first data on the basis of the first information and the second information. The processing unit (12) optimizes the first information and the second information so as to reduce the value of a loss function relating to the plurality of pieces of first data and the plurality of pieces of second data.

The application provides a kind of sports non-material cultural heritage protection policy intervention effect evaluation method, comprising: collecting multi-source heterogeneous data, extracting structured policy-behavior-response elements, constructing dynamic policy knowledge graph and realizing the time series modeling of causal network;Adopting time series contrast learning encoder and twin network optimization policy variable embedding, realizing accurate extraction of policy effect after mixed variable elimination;Integrating do-calculus and Monte Carlo tree search for counterfactual causal reasoning, forming multi-path effect contribution and stability evaluation;Through Shapley value decomposition and visual report, the net causal effect of each policy tool is quantified and the effectiveness is determined, the application can realize multi-dimensional causal effect accurate identification for sports non-heritage policy intervention process, improve the scientificity and timeliness of attribution analysis.

The invention discloses a front door adjustment-based harmful modal cause detection depolarization method, which comprises the following steps of: constructing a structural causal model, creating a back door path between a modal cause and a harmful label by using unobserved confounding factors, and eliminating harmful modal cause detection through front door adjustment; the front door adjustment then blocks the back door path by introducing reasoning as an intermediary between the memetic and the tag. The method comprises the following steps: decomposing the causal effect of a medal factor on a harmful tag into the influence of the medal factor on an intermediary, quantifying by adopting a multi-modal large-scale language model with different beam searches to calculate the probability of each reasoning sequence and the influence of the intermediary on the tag, and performing effective approximation through a normalized weighted geometric averaging method; finally, post-interpretation is synthesized from the inference sequence and the predictive tag using a large language model. According to the scheme, the most advanced performance is achieved while information interpretation is provided.

The invention relates to the technical field of automatic driving, in particular to a causal effect-based robust automatic driving trajectory prediction method, which comprises the following steps of: establishing a causal graph of a vehicle trajectory prediction model in an attack scene, and analyzing a causal relationship among nodes in the causal graph; building the fact prediction of the vehicle track in the attack scene according to the historical track, leading anti-fact intervention on the historical track, and building the anti-fact prediction of the vehicle track in the attack scene; and calculating a direct total effect by subtracting the anti-fact prediction from the fact prediction, and taking the direct total effect as a final prediction result. According to the method, the direct total effect in causal reasoning is used for defending the adversarial attack, and compared with an existing defending method, the adversarial robustness of the trajectory prediction model under the attack scene is effectively improved at the cost of sacrificing small performance on a clean data set.

The invention discloses a drug relocation prediction method based on causal inference. The method comprises the following steps: 1) constructing a multi-modal heterograph; 2) obtaining a medicine functional embedding expression; 3) calculating a causal intensity weight of a drug-disease edge based on Do-calculation, and constructing a causal perception heterograph; 4) constructing a structural causal model, and calculating the average treatment effect of the drug on the disease; 5) designing a causal heterogeneous graph convolutional network, and learning node deep causal characterization; 6) identifying and correcting bias and errors; (7) What-if analysis is conducted through an anti-fact reasoning module, and the difference between an anti-fact result and the effect is calculated; and 8) fusing node deep causal characterization, a causal intervention result and an anti-fact reasoning conclusion, outputting a drug-disease causal association probability, and tracing a key causal path and an action mechanism. According to the method, the problem that the causal effect and the false correlation are difficult to distinguish in a traditional drug relocation method is solved, and the reliability, the interpretability and the generalization ability of a prediction result are improved.

The invention discloses a process root cause positioning method and system based on causal resonance attenuation, and the method comprises the steps: firstly collecting key node data of a process event chain, and constructing a state vector of the process event chain; then based on the state vector of the process event chain, constructing an event transfer operator; based on an event transfer operator, carrying out chain type evolution on a causal wave packet in a whole process event chain, and calculating causal wave packet energy; and finally, based on causal wave packet energy, calculating an energy attenuation rate, and positioning an energy loss root cause event in the process event chain. According to the method, a modeling mechanism of'causal wave packet 'and'event transfer operator' is embedded in a process event chain, and from the angle of causal energy transfer, the attenuation degree of each event on the overall chain causal effect is quantified in a complex business process, so that accurate positioning of a real disease cause node in the complex process is realized, and the accuracy of the process flow is improved. And an explainable and verifiable evidence chain is provided for project management and cross-department collaboration.

The invention discloses a matching-based privacy-preserving average causal effect estimation method and system. The method comprises the following steps: firstly, training a logistic regression model, and injecting noise into a model weight; thirdly, calculating the tendency score of each sample by using the disturbed model, and adding noise; then calculating the distance between different samples according to the tendency score of each sample after disturbance, and sorting the distance; thirdly, matching is achieved based on the ranked distance matrixes, so that the potential intervention result of each sample under disturbance setting is calculated, noise is further injected into the sum of the potential intervention results of all the samples, and estimation of the average causal effect is achieved based on the sum of the potential results of disturbance.

The application discloses a kind of causality effect robustness inspection method based on counter disturbance, it is related to geological disaster risk analysis technical field, including: based on causal inference method obtains the causal factor of geological disaster prediction model, associated factor, and the causality effect index of corresponding factor;Factor disturbance space with physical rationality constraint is constructed, and the value range of factor disturbance amount is defined;Zero-order optimization counter attack method is used to generate factor-level counter disturbance samples in factor disturbance space;The change amount of model output before and after factor applies counter disturbance is calculated with the causality effect change amount, and factor-level counter robustness index is obtained;Causal robustness index is constructed, and the reliability evaluation and classification of causal factor are completed.The application solves the problems that the existing technology lacks robustness verification of causal effect, the causal factor may be affected by counter disturbance and fail, lacks causal robustness evaluation system of factor level and cannot identify pseudo-causality in causal inference result.

Methods, systems, and media for estimating the causal effect of different content exposure levels are provided.

The invention provides a video feature extraction method and device, equipment and a storage medium, and relates to the technical field of video processing. According to the method, the basic features of the video frames are extracted through the convolutional neural network, and the key visual information of the video frames is reserved. The learnable projection matrix is utilized to map the basic features into causal node representation and construct a causal graph, so that the feature representation is more semantic and explanatory. And performing anti-fact intervention operation on a target intervention node in the causal graph, calculating a causal effect value, and further strengthening causal association between the features. And adjusting the standard attention weight based on a causal regularization attention mechanism to generate a causal regularization feature. And inputting the causal regularization features into the long short-term memory network, performing frame-by-frame state updating and picture prediction on the video frame sequence, and effectively capturing the time sequence causal relationship of the video, thereby improving the understanding ability and prediction accuracy of video scenes in the financial and medical fields.

The invention provides a membrane pollution mechanism identification, regulation and control method based on causal inference. The method specifically comprises the following steps: collecting multi-dimensional feature data of a membrane pollution behavior in an operation process of a membrane separation system; performing data processing on the multi-dimensional feature data, and constructing a structured membrane pollution behavior causal mechanism analysis data set; preliminarily constructing a directed acyclic graph of a prior causal relationship to obtain a causal path; estimating an average causal effect between a processing variable and a result variable in each causal path by adopting a non-parametric dual machine learning method under an EconML framework; quantifying the total effect intensity of the complete causal path, and identifying a key driving path influencing membrane pollution; and implementing targeted regulation and control according to the key driving path and the action mechanism thereof. The invention provides a causal inference method suitable for a membrane pollution formation system, and identification of a real causal path between variables and quantitative estimation of a causal effect are realized by constructing a causal structure model.

The invention relates to the technical field of artificial intelligence, and discloses an Internet big data analysis method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous Internet big data, and carrying out the preprocessing of the multi-source heterogeneous Internet big data into a standardized time sequence multi-dimensional data set; potential causal variable characterization is extracted through a variational auto-encoder; constructing a dynamic causal structure map satisfying time constraint and statistical significance; estimating a quantitative causal effect by adopting dual robustness; and executing anti-fact inference and reinforcement learning driven decision optimization based on the causal atlas. The system comprises a data acquisition and preprocessing module, a causal variable extraction module, a causal atlas construction module, an effect quantification module and a decision optimization module. According to the method, intelligent decision transition from correlation analysis to causal driving is realized, the interpretability, robustness and generalization ability of the model are remarkably improved, and low-cost anti-fact deduction and automatic strategy generation are supported.

The invention discloses an interaction quantity causal contribution prediction method and device and a storage medium, and relates to the technical field of data processing, the method comprises the following steps: executing an anti-fact intervention operation on a target label in a label set based on a preset intervention value, and generating an intervention label code; performing feature extraction and re-parameterization sampling on the intervention label codes and the environment variable codes to obtain causal latent variables after intervention; performing iterative causal propagation on the post-intervention causal latent variable, and splicing the post-intervention causal latent variable and an additional latent variable after propagation to obtain a post-intervention latent variable; performing result prediction on the post-intervention latent variable based on a decoder to obtain a post-intervention prediction interaction amount; determining an arithmetic mean value of the post-intervention prediction interaction quantity and a difference value of the initial prediction interaction quantity as an average causal effect of the target label; and the accuracy of label effect evaluation is improved.

The invention discloses an intelligent decision support method and system based on causal inference, and relates to the technical field of data analysis, and the method comprises the steps: reading the operation multivariate structure data of each historical digital advertisement service, finding and analyzing the causal structure of the operation multivariate structure data according to a mixed causal structure, and obtaining the causal structure of the operation multivariate structure data; establishing a causal directed acyclic graph of each digital advertising service; performing causal effect identification estimation according to an operation multivariate structure data causal structure in the causal directed acyclic graph, marking an effect state between causal variables, and establishing a causal directed weighted acyclic graph of each digital advertising service; and analyzing stability and heterogeneity between causal structures of each path in the causal directed weighted acyclic graph, substituting the stability and heterogeneity into the digital advertisement marketing causal decision model, and generating a causal influence decision support scheme of each digital advertisement service. The method has the advantages that a reliable basis is provided for optimal configuration and personalized marketing of advertisement resources, and the rate of return on investment and the operation efficiency are greatly improved.

The invention discloses a material preparation process data causal quality evaluation method and system under large language model collaboration, and the method comprises the steps: building a whole-flow process directed graph of "component-structure-process-performance", and achieving the structuralization and semantic representation of data; then fusing a graph neural network and causal reasoning, quantifying an individual causal effect of process parameters on material performance based on anti-fact intervention, and accurately identifying key quality control points; a large language model three-stage cooperation mechanism is introduced, domain knowledge priori generation, reasonable anti-fact construction and result semantic calibration are achieved, and intelligence and reliability of the evaluation process are ensured; according to the method, the limitation of traditional correlation analysis is broken through, the crossing from shallow anomaly detection to deep causal diagnosis is realized, the material process data quality management level and the optimization decision efficiency are improved, and data support and quality guarantee are provided for digital twinning construction.

The invention discloses a diaphragm dysfunction early warning system based on causal reasoning and deep learning, and the system comprises a data collection module which is used for collecting multi-modal physiological data; the data preprocessing module is used for performing time alignment, denoising and normalization processing on the multi-modal physiological data; the multi-modal feature extraction module is used for carrying out global time sequence feature modeling and local causal feature modeling through an improved PatchTST network; the causal structure learning module is used for generating a sparse causal adjacency matrix through an improved NOTEARS causal reasoning algorithm; the causal effect analysis module is used for generating a high-sensitivity variable set; the risk early warning module is used for generating diaphragm dysfunction early warning information; and the incremental updating module is used for performing incremental updating on the improved PatchTST network and the improved NOTEARS causal reasoning module. According to the invention, the accuracy, sensitivity and causal interpretation ability of diaphragm dysfunction early warning are improved.

The invention provides a rotating machine fault positioning method based on double difference method causal inference. The method comprises the following steps: firstly, acquiring rotating machine state data in normal and fault states; then constructing a DID causal inference model, defining a processing group and a control group, and calculating by using the DID causal inference model to obtain an observation value; using the fault with the marked fault type and the normal state data as training samples, and establishing and training an LSTM deep learning model; inputting actually collected to-be-analyzed data into the LSTM deep learning model, and judging whether a fault exists or not and a fault type result; and if the fault exists, taking the real set data as a processing group, taking the normal data as a control group, calculating an observation value of the processing group compared with the control group by using a DID causal inference model, and judging the fault influence degree and the fault occurrence position. According to the method, the processing group and the control group are defined, the causal effect of the processing group and the control group is effectively estimated, and the output of the deep learning model and the causal inference result of the DID are fused, so that more accurate fault positioning is realized.

The invention discloses a hidden variable identification method and device based on causal analysis, equipment and a storage medium, and relates to the technical field of artificial intelligence and causal inference, in the whole process, information gain is used for dynamically quantifying a variable causal effect to realize hidden variable identification, so that a hidden variable judgment result can reflect the real existence of the hidden variable instead of data noise, and the hidden variable identification accuracy is improved. And the judgment accuracy is obviously improved. The method comprises the steps that iteration judgment is carried out on observation variables in an observation variable data set to obtain causal information gains of target observation variables obtained through iteration judgment, the observation variable data set comprises a plurality of observation variables, and the target observation variables are observation variables meeting shielding conditions in each iteration judgment process; the shielding condition is that no direct causal relationship exists between the observation variable and the target variable; calculating an average information gain and a final non-fitting error according to the causal information gain of the target observation variable; and if the average information gain is smaller than the final non-fitting error, determining that the target system has the hidden variable.

The present application relates to a kind of gas stove operation chain risk prediction method and system based on causal inference, belong to gas risk prediction field.Therein, the method includes collecting original data and forming multivariate time series;Time series causal structure learning is carried out based on multivariate time series, and time-delayed time series causal directed acyclic graph is output;Causal effect quantification is carried out based on multivariate time series and time series causal directed acyclic graph, and the causal effect function of each edge is obtained, and structural causal model is output;Counterfactual path deduction is carried out based on multivariate time series, time series causal directed acyclic graph and structural causal model, and a group of possible future causal paths are obtained, and each path corresponds a probability value;Chain risk prediction is carried out based on future causal path and its probability value, and early warning information is obtained.The present application provides a kind of gas stove safety warning method capable of understanding risk transmission mechanism, identifying key causal path, providing interpretable early warning.

The present application relates to a kind of medical insurance expense abnormality tracing method based on multi-view clustering and causal inference, belong to medical big data analysis and artificial intelligence technical field, for medical insurance settlement data extraction heterogeneous feature view set;Subsequently, using consensus learning algorithm based on adaptive weight, each view is mapped to low-dimensional consensus representation space;Then, by calculating the view conflict score and outlier distance of sample, screening suspected abnormal settlement sample.For suspected abnormal sample, construct structured causal model, use causal effect Quantification to strip the interference of mixed factors such as severity of illness;Subsequently, by intermediary effect analysis, the path mechanism of abnormality is extracted, and the subject of responsibility is determined based on counterfactual reasoning.According to the attribution result, generate structured evidence chain, and combine the audit feedback mechanism to dynamically update clustering weight and causal graph topology, realize the classification and hierarchical supervision of medical insurance violation behavior.