104 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 personalized information accurate pushing system and method based on artificial intelligence, and relates to the technical field of personalized recommendation, and the method comprises the steps: fusing user multi-platform behavior data and external space-time environment information, and generating a situation label with confidence through employing an improved space-time density clustering algorithm; constructing a causal directed acyclic graph by adopting a causal forest algorithm, quantitatively analyzing a heterogeneity causal effect, inverting a potential intention of the user, and outputting standardized intention inversion probability distribution; in combination with a historical intention and a behavior sequence, training a Transform intention state transition model constrained by causality of a causality directed acyclic graph, and performing multi-step probability deduction to generate an intention evolution path; information is retrieved from the dynamic knowledge graph according to the prediction path, a pushing copywriting matched with the situation is generated through the NLP technology, the optimal pushing opportunity is calculated in combination with the position track of the user, and accurate reaching of personalized information is achieved.

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.

The invention discloses a user behavior prediction method based on a causal tendency score. The method comprises the following steps: collecting historical behavior information of a to-be-detected user from a historical database; carrying out behavior feature extraction, and carrying out normalization processing on behavior features; establishing a behavior prediction model; model parameters are adjusted through a back propagation algorithm and an optimizer; analyzing a causal relationship between the behavior characteristics; analyzing a reason for generating a prediction error result by the behavior prediction model; and performing user behavior prediction by using the optimized behavior prediction model, and making an optimization strategy according to a causal relationship revealed by the tendency score. According to the method, deep mining and quantitative evaluation of the causal relationship behind the user behavior are realized by constructing the feature association network, screening the causal candidate pairs, quantifying the causal effect and calculating the causal tendency score, so that a root cause of a behavior prediction error is disclosed, and the interpretability and decision support capability of the model are also remarkably improved.

The invention discloses a deep forgery detection interpretable method, system and device based on causal analysis and a medium, and belongs to the field of face deep forgery detection. The method comprises the following steps: acquiring a multi-source deep counterfeiting data set, extracting a face region, carrying out key point alignment, and carrying out preprocessing; constructing a structured causal model, abstracting the deep counterfeiting detection model into the structured causal model, and defining an endogenous variable and an exogenous variable; and inputting the forged data sets with different depths into the structured causal model, calculating the average causal effect of each neuron in the deep forging detection model, identifying the neuron having important potential for the generalization ability of the model, and calculating the intersection of the first n contribution neuron of the detection model on different data sets to obtain the depth of the deep forging detection model. Generalization neurons shared across the data sets are screened; and carrying out face deep forgery detection by using the final deep forgery detection model. According to the method, the detection precision of the deep forgery detector is improved, and the method has important potential to adapt to unknown forgery data sets.

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 causal variation inference-based confounding factor-removing multi-behavior recommendation method, which comprises the following steps of: encoding potential uncertainty in multi-behavior interaction by adopting a variation graph automatic encoder so as to capture heterogeneity among different behaviors; in order to efficiently deduce potential confounding factors, a confounding factor reasoning mechanism is designed, and the potential confounding factors are generated through variational reasoning. In a forward diffusion stage of the conditional diffusion module, the model gradually injects noise into potential variables of users and articles to simulate dynamic evolution of user preferences along with time and different situations. In the back diffusion stage, the model uses the deduced confounding factors and combines causal reasoning to guide the denoising process, the influence of potential confounding factors is relieved, and the real causal effect of multi-behavior interaction is captured. Experiments are carried out on two public data sets by aiming at ten recommendation algorithms of four different research emphasis, and experimental results show that the performance of the method is superior to that of existing algorithms of the same type.

Example embodiments described herein provide a two-stage approach for training, on a dataset of samples received as input, a structural causal model (SCM). In a first stage of the example two-stage approach, a trained causal ordering predictor is used to infer a causal order of variables from the dataset. In a second stage, the SCM is trained on the same dataset using the predicted causal ordering from the first stage. Once trained, the SCM may be used to predict a causal effect of an action on a target system.

The invention discloses a time sequence prediction method based on cumulative causality and application, and belongs to the technical field of time sequence prediction.The method comprises the steps that firstly, a structural causality model is established for a power system containing observable reason variables, unobservable time-varying reason variables and to-be-predicted target variables; constructing an initial value and a time variation value of an encoder network characterization unobservable reason variable; and establishing a dynamic convolutional neural network prediction model by using the cumulative causal effect of a time-varying reason variable and a time-varying mechanism of convolutional expression, and outputting a result through a mask layer and accumulation. The prediction method based on the accumulative causal effect and the application are suitable for time sequence prediction problems such as metal component aging deformation prediction and the like, and have good potentials of stripping false correlation and improving time sequence prediction precision and stability.

The embodiment of the invention discloses a data processing method, in the method, first intervention data of an intervention variable in a decision service can be utilized to perform causal effect estimation on the decision service based on an anti-fact thought, and anti-fact intervention data obtained based on the anti-fact thought can be a continuous variable. The variable can be a discrete variable, so that the causal effect estimation can be carried out on various types of intervention variables in various decision service scenes. Besides, in the method, the causal effect model can perform causal effect estimation on the input intervention variable in a group corresponding to the input confusion variable so as to obtain an output result about the result variable. Thus, the anti-fact intervention data and the first confusion data are input into the causal effect model, the first estimation result can be obtained through the causal effect model, and for a group described by the first confusion data, accurate causal effect estimation is obtained according to the anti-fact intervention data.

The invention discloses a medical data enhancement method and device based on causal inference, a storage medium and equipment, and the method comprises the steps: obtaining and preprocessing original medical data of an existing actual case, and obtaining to-be-enhanced medical data; constructing a causal graph according to the to-be-enhanced medical data; wherein the causal diagram is used for representing the causal relationship among the disease, the symptom and the treatment scheme; performing causal effect estimation on the to-be-enhanced medical data to obtain a weighted data set; performing anti-fact inference by using a potential result framework based on a causal graph and the weighted data set to obtain a virtual case; and generating an enhanced medical data set according to the virtual case and a preset generative adversarial network. Therefore, by adopting the embodiment of the invention, the introduction of data which does not conform to the medical law can be avoided through the causal relationship modeling, so that the quality of the generated data is high, and the performance of the AI model can be effectively improved.

The invention discloses an individualized drug dose optimization method based on causal traceability, which improves the accuracy and clinical adaptability of dose regulation and control through a multi-modal space-time alignment and feature decoupling technology, causal reasoning modeling and causal effect quantification and individualized dose optimization, can effectively reduce adverse reactions in the treatment process, and improves the treatment efficiency. The life cycle of a patient is prolonged, the life quality is improved, meanwhile, the long-term medication cost is reduced, and wide clinical application prospects and popularization values are achieved.

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 causal influence discovery-based disguised topological element attack method. The method aims at solving the problems that an existing attack method does not distinguish a disturbance total graph, neglects attack budget and node differences, does not select key topological elements, is low in resource utilization rate, is limited in success rate and the like. According to the method, graph structure elements susceptible to attack are discovered and perceived by means of causal influence, fragile nodes are identified by using a layered filtering mechanism, and accurate allocation of resources is realized; integrating causal effect evaluation to determine key nodes; and carrying out optimal element operation by adopting a joint loss optimization disturbance method so as to maximize the attack effect in a prediction. Experiments show that CTEA is superior to an existing method in efficiency and effect, and the CTEA shows adaptability, robustness, accuracy and resource efficiency on multiple GNN architectures and data sets.

Methods and systems for estimating causal effects from knowledge graphs are provided. The method includes obtaining intervention application data and subject history data for a candidate group of subjects, and dividing, based on the intervention application data, the candidate group into a reception subgroup that received an intervention and a rejected subgroup that did not receive the intervention. The method includes for each subject within the candidate group, mapping, based on the subject history data, a covariate value set onto a knowledge graph with an embedding neural network; and for each subject in the reception subgroup or the rejection subgroup, translating the covariate value sets for the subjects within the reception subgroup or the rejection subgroup into a reception matrix or a rejection matrix with a feature neural network. The method includes comparing the reception subgroup to the rejection subgroup to determine a differential intervention effect.

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 cause and effect inference method based on problem disassembly and algorithm matching, and relates to the technical field of cause and effect inference methods, and the method comprises the following steps: S1, defining a cause and effect inference problem; according to the cause-and-effect inference method based on problem disassembly and algorithm matching, a cause-and-effect inference problem is disassembled into four types of sub-problems, and a complete cause-and-effect inference algorithm matching rule is designed, so that a complex problem in a cause-and-effect inference process can be effectively solved; through six steps of causal inference problem definition, causal inference data set generation, causal relationship identification and inspection, causal effect estimation and inspection, anti-fact inference calculation and causal traceability analysis, full coverage of four types of sub-problems of causal inference is realized, and multiple causal relationship discovery means can be provided. And a suitable causal effect evaluation algorithm is automatically matched for each pair of causal variables, so that the accuracy of causal inference results such as causal relationship identification and causal effect calculation is ensured, and good adaptability is achieved.

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 present invention discloses a multivariable Mendelian randomization method for inferring the causal relationship between images and phenotypes, including integrating genomic, radiomic, and phenomic data for causal inference between brain imaging feature metrics and phenotypes, considering the correlations of multiple brain imaging feature metrics in the causal inference and correcting possible biases, specifically including the following steps: S1: Obtain initial instrumental variables, risk factor variables, and phenotypes; S2: Bias correction, screening the initial instrumental variables and risk factor variables; S3: Use a two-stage method to infer the causal relationship and screen out the brain imaging feature metrics that have a causal relationship with the phenotype; S4: Use Sargan for horizontal pleiotropy test to check whether the part of the said phenotype that cannot be explained by the risk factor is significantly correlated with the initial instrumental variable. Compared with traditional methods, the present invention can obtain more accurate causal effect estimates, and the type I error rate of the selected risk factors is lower.

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.

The application discloses a face feature decoupling representation method and device with causal effect transmission and a medium, relates to the technical field of causal discovery, image generation and decoupling representation learning. The method comprises the following steps: establishing a variational autoencoder, learning a latent feature distribution from a latent space based on the variational autoencoder in response to input face data; integrating a structural causal model into the variational autoencoder, modeling a causal effect to extract a causal feature from the latent feature distribution; establishing a graph attention network, inputting the causal feature into the graph attention network to decouple and transmit the causal effect; and designing a loss function, training the graph attention network by using a discriminator. The method can effectively mine and transmit the causal relationship between face features, improve the interpretability and transferability of feature representation, has strong application potential, and is especially suitable for face recognition, expression analysis and other tasks.