77 results about "Causal structure" patented technology

Methods, systems, and computer-readable media for causal training of physics-informed neural networks (PINNs). The shortcoming of conventional PINNs may be due to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. This is a fundamental limitation and a key source of error that ultimately steers FINN models to converge towards erroneous solutions. Methods can include a re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. This modification alone is enough to introduce significant accuracy improvements, allowing us to tackle problems that have remained elusive to PINNs.

The invention discloses an artificial intelligence data analysis method and system based on machine learning, and the method comprises the following steps: 1, collecting and standardizing original data, and carrying out the embedding processing, and obtaining a data feature vector; 2, mapping the data feature vector into a Riemannian manifold space; 3, calculating the distance between every two sample points by using Gromov-Hausdorff measurement, and establishing an initial causal latent map; 4, constructing a causal potential energy tensor field; 5, obtaining an evolved causal latent map by adopting a Ricci curvature disturbance mechanism; 6, calculating a geodesic distance in the evolved causal latent map as an information propagation path; 7, constructing a causal relationship model; and 8, executing anti-fact reasoning based on the causal relationship model, simulating the causal influence of input variable disturbance on an output result, and performing visual output. According to the method, Riemannian manifold embedding and curvature evolution methods are fused, and intelligent data analysis of causal structure modeling is realized.

The invention discloses a heterogeneous time series data fault causal tracing method, which comprises the following steps of: converting discrete variables into hidden continuous variables with higher information granularity through context adaptive excitation Gaussian kernel embedding, and further realizing causal discovery in a unified continuous space; in the potential continuity recovery stage, guiding information of continuous variables is introduced by designing a prediction task, parameters of context self-adaptive excitation Gaussian kernel embedding are adjusted in a self-supervised mode, the potential continuity recovery capacity is enhanced, and reversibility of the recovery process is ensured through a reconstruction mechanism; in the causal structure learning stage, key causal relationships are screened through sparse regularization constraints, an overall causal graph is constructed, and support is provided for fault analysis. According to the method, the time sequence causal relationship can be accurately determined in a heterogeneous variable scene, important reliable information is provided for root cause traceability of process faults, accurate diagnosis of fault sources is assisted, and therefore the safety of actual production is effectively guaranteed.

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 relates to the technical field of intelligent building heat load prediction and heat balance regulation and control, and discloses an intelligent building heat balance dynamic regulation and control method and system based on load prediction, and the method comprises the steps: constructing a time-frequency causal double-flow analysis network, mutual enhancement of the time-frequency characteristics and the causal relationship is realized; designing a hierarchical causal discovery algorithm, and mining a multilevel causal structure; developing a causal enhanced time-frequency representation learning method, and fusing time-frequency and causal information; establishing an intervention decision framework based on anti-fact analysis, and evaluating an intervention effect; an abnormal mode self-evolution recognition system is realized, and new abnormal modes are continuously learned; an interpretable abnormity diagnosis mechanism is developed, and a clear diagnosis report is provided; according to the method, the thermal load prediction accuracy is improved, the abnormal early warning capability is enhanced, the intervention efficiency is improved, the system interpretability is enhanced, and the optimization of the energy utilization efficiency is realized.

The invention provides an anti-fact fair data synthesis method and device based on causal reasoning, and aims to generate high-quality synthesis data meeting the fairness requirement by mining the causal relationship between observable features. The synthesis method comprises the following steps: extracting observable features, sensitive features and labels from original data, extracting potential features through a variational automatic codec, and constructing a causal relationship graph; designing a generator according to a topological sequence of the causal relationship graph, connecting a causal path, inputting the potential features and the related features into the generator in sequence, and constructing a data generation process conforming to a causal structure; introducing a discriminator to carry out adversarial training on a generation result and original data, and optimizing generator parameter distribution; finally, synthetic data meeting fairness requirements are generated. According to the method, effective regulation and control on the influence of sensitive characteristics and strict constraint on a causal structure are realized, the generated data has higher fairness and interpretability, and the method can be applied to the fields with higher fairness requirements, such as finance, medical treatment and education.

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention provides an unmanned aerial vehicle fault important characteristic parameter optimal selection method and system based on causal reasoning and a medium, and the method comprises the steps: S1, collecting fault data which comprises flight fault data collected by a gyroscope, a GPS, an airspeed meter and the like; s2, performing data processing on the fault data; s3, causal structure learning: performing causal structure learning on the data after data processing through a VAE decoder and a VAE encoder in sequence to obtain a weighted causal DAG representing a causal relationship between the features; and S4, important fault feature selection: after the weighted causal DAG is obtained, node scores are calculated, then the node scores are sorted in a descending order, the top F fault feature variables are selected as feature subsets, and F is a positive integer. The method can effectively screen out the characteristic parameters of the fault of the unmanned aerial vehicle, can be used for fault diagnosis, facilitates the reduction of the fault frequency of the unmanned aerial vehicle, and improves the safety and reliability of the unmanned aerial vehicle.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

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 relates to the field of data processing and information technology operation and maintenance, and discloses an intelligent operation and maintenance decision-making method and system based on multivariate heterogeneous data fusion. Comprising the following steps: constructing a dynamic causal graph reflecting a system state and an environmental factor causal relationship based on a causal structure learning algorithm; constructing a multi-agent game decision model based on the dynamic causal diagram, and respectively setting a global operation and maintenance target and a component local demand as a leader strategy and a follower strategy of the game; and solving game equilibrium by using multi-agent reinforcement learning, and outputting an optimal joint operation and maintenance decision instruction. The system is composed of a data acquisition preprocessing module, a multi-modal feature fusion module, a causal structure learning module, a game decision solving module and an execution monitoring module. According to the method, the causal inference and the game theory are fused, so that the causal logic can be accurately extracted, the decision robustness and interpretability are improved, and the global optimal collaborative configuration is realized.

The invention discloses a multi-modal condition-driven closed-loop sensing and generation optimization method and device and a medium, which are applied to an automatic driving long-tail scene, firstly, multi-modal conditions such as text description, a semantic map and a 3D layout are uniformly represented, a causal inference network is introduced, a causal structure between scene elements is explicitly inferred from multi-modal input, and a multi-modal condition-driven closed-loop sensing and generation optimization model is obtained. Generating structured causal embedding to realize causal perception enhancement of generation conditions; in a diffusion generation stage, a causal consistency mechanism is deeply fused into a condition control and denoising process, and a diffusion model is guided to effectively inhibit generation of unreasonable or common sense violating scenes in a generation process through an anti-fact condition constructed by a causal inference network, so that semantic reasonability, spatial consistency and dynamic credibility of long-tail data are remarkably improved. According to the method, the problem that long-tail scene data is deficient is solved, causal constraints are introduced to a generation source, and the reliability, robustness and cross-domain generalization ability of an automatic driving system in an extreme scene are remarkably improved.

The invention relates to a causal structure constraint and inference method and system based on a large language model, and the method comprises the steps: inputting a variable understanding text and a variable understanding cue word to LLMs to output an understanding variable text; inputting into LLMs based on the understanding variable text, and combining a causal inference cue word to output a directed graph network set A1; inputting the initial directed graph network set A1 and an error correction prompt word into LLMs to output a corrected directed graph network set A; and taking the directed graph network set A as an ancestor constraint, and incorporating the ancestor constraint A into a score-based causal structure learning algorithm to obtain an optimal causal structure. According to the scheme, more accurate and credible causal relationship inference can be provided when a complex data set is processed.

The invention discloses a dangerous scene linkage disposal method based on causal reasoning. The method comprises the following steps: collecting and preprocessing multi-source heterogeneous dangerous scene data; executing causal structure learning to generate a dangerous event causal structure skeleton; introducing an improved LiNGAM model to obtain a dangerous event causal directed acyclic graph; forming a dangerous event causal reasoning model; executing causal reasoning calculation to generate a causal influence path; performing risk assessment to generate a risk level label; issuing to a target linkage subsystem to form a linkage processing instruction set; obtaining linkage processing execution feedback data; and outputting the optimized causal reasoning model, realizing accurate identification and dynamic updating of the causal relationship of the dangerous event, and improving the response speed and the disposal precision of the system in a complex environment.

The invention relates to a causal structure discovery method based on evolutionary neural architecture search and reinforcement learning, and the method comprises the steps: obtaining to-be-detected observation data, inputting the to-be-detected observation data into a causal structure detection model, and obtaining a directed acyclic graph composed of highest confidence coefficient edges; the causal structure detection model is obtained by training an Actor-Critic model by using a training set; the Actor-Critic model comprises an actor network model and a commentator network model; in the training process, the model configuration of the Actor-Critic model is optimized based on an evolutionary algorithm, and the optimal configuration is obtained; and inputting the observation data in the training set into the actor network model under the optimal configuration to obtain a candidate directed acyclic graph, and evaluating the candidate directed acyclic graph by the commentator network model under the optimal configuration to assist in strategy optimization to obtain a candidate directed acyclic graph. And strengthening the actor network model under the optimization strategy through a reinforcement learning algorithm to tend to output an optimal causal graph structure.

The invention discloses a natural gas hydrate productivity main control factor analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining sample data of various variable factors of natural gas hydrate productivity, and carrying out the preprocessing of the sample data; constructing a preliminary causal structure between variable factors based on a physical mechanism, and performing direct causal relationship evaluation based on the preliminary causal structure and the preprocessed sample data to obtain a target causal graph; and performing causal inference analysis based on the target causal diagram to obtain main control factors of the natural gas hydrate productivity. According to the natural gas hydrate productivity main control factor analysis method based on causal inference, the causal relationship of productivity influence is accurately identified, the scientificity and reliability of main control factor screening are improved based on the causal relationship, and more powerful technical support is provided for natural gas hydrate development. The method can be widely applied to the technical field of data processing.

The invention provides a key test factor determination method and device based on a Bayesian causal network, and relates to the technical field of reason tracing. The method comprises the following steps: acquiring a multi-dimensional observation parameter of a preset intelligent system, and generating an initial data set based on the multi-dimensional observation parameter; based on an improved Bayesian causal structure learning algorithm and the initial data set, determining a causal structure diagram between an input factor and a target output in the initial data set; based on the causal structure diagram, performing intervention operation on an input factor pointing to the target output to determine an average causal influence degree of the input factor on the target output; and calculating importance degree scores of the input factors according to a preset weight fusion mode and the average causal influence degree, and determining the input factors corresponding to the importance degree scores meeting a preset key factor selection condition as key test factors. In the intelligent system, the real causal-driven effect between the variables is effectively determined, and the accuracy of the key test factor is improved.

The invention relates to a grouping-based root cause positioning method in a high-dimensional time sequence, which comprises the following steps of: firstly, dividing an original variable into a plurality of intra-group subspaces by constructing a variable grouping matrix, so as to construct a sparse group-level causal graph with a priori structure at a group level; and then, the group-level causal diagram is refined to a variable-level causal diagram layer by layer through a mapping mechanism, and in combination with an exogenous variable modeling strategy, a reachable path defined in a causal structure is utilized to perform structure-guided reconstruction on an observation value. And finally, in a root cause analysis stage, the model further calculates a root cause score based on the exogenous disturbance quantity of each variable so as to realize refined root cause analysis. Meanwhile, the experimental result shows that the causal relationship of the time sequence can be accurately captured, and the causal relationship of the abnormal phenomenon can be effectively identified; a group-level causal mapping and structure-guided exogenous disturbance modeling method is introduced, so that a causal structure can be remarkably stabilized in a high-dimensional scene, and the root cause positioning accuracy is improved.

The invention relates to the technical field of machine learning and fault diagnosis, in particular to a causal-driven credible cross-component mechanical fault diagnosis method and system. According to the method, a causal structure model composed of a feature extraction module and a relation measurement module is constructed under a meta-learning framework to guide a causal decomposition module to mine an internal causal mechanism of vibration signal and fault category mapping, and relation measurement function prediction and fault category prediction are improved by using multi-task collaborative optimization. Therefore, the model can quickly adapt to a new target component task, the overall diagnosis efficiency is improved, and the classification accuracy and robustness are excellent. The problem that the application range of combination of small sample learning and a causal theory is small or a causal path is easy to omit in the prior art is solved.

The application relates to an excitation system fault recording and event record analysis and diagnosis method and system, belonging to the field of excitation systems. The method comprises collecting recording files and event sequence records of the excitation system; performing multi-domain feature extraction based on the recording files to obtain a multi-domain feature tensor; performing space-time causal structure learning based on the multi-domain feature tensor and the event sequence records to output a causal adjacency matrix, a causal diagram and a time delay matrix; performing double-channel interpretable fault classification based on the multi-domain feature tensor, the causal diagram and an event time tag list E in the event sequence records to output a fault type label M and an attention space-time heat map; performing counterfactual causal tracing to obtain a root cause variable set and a causal propagation path, and outputting a diagnosis report. The application realizes intelligent diagnosis of the excitation system with signal analysis capability, causal reasoning capability and diagnosis interpretability.

The invention provides a multi-source data-oriented collaborative causal structure learning method and system, and relates to the technical field of causal discovery. The method comprises the following steps: firstly, acquiring a multi-source observation data set, and performing initial clustering to obtain an initial causal structure model; and updating and clustering the data set based on the initial causal structure model to obtain a clustering distribution result, and updating the causal structure model. And taking the updated causal structure model as a new initial causal structure model, iteratively executing updating clustering and model updating steps until a preset convergence condition is met, and finally outputting a clustering allocation result and the causal structure model. According to the collaborative causal structure learning method provided by the invention, the fault data sample is effectively expanded, and the accuracy of a causal structure learning result is improved.

This invention discloses a document-level debiased sentiment analysis method, system, and storage medium. The method includes: pre-constructing a structured causal model; extending the structured causal model by encoding comment text and aspect words, and calculating interaction node representations; forming a front-door path based on the aspect words through the interaction node representations; introducing shared confusion factor nodes, and generating causal structure edges for the shared confusion factor nodes, comment text, aspect words, and front-door paths; constructing a causal structure graph using the causal structure edges; transforming the causal structure graph into a heterogeneous graph neural network; calculating the total effect on sentiment using an intervention procedure on the heterogeneous graph neural network; analyzing the polarity input of the comment text, aspect words, and interaction node representations based on the structured causal model to obtain a task loss function; and converging the extended structured causal model based on the task loss function.

This invention discloses an incremental federated causal structure learning method in dynamic scenarios, involving the interdisciplinary field of computer causal inference and federated learning. The method includes the following steps: S1: Historical federated causal structure learning; S2: Adaptive selection of new clients; S3: Weighted federated causal structure learning. A weighted federated causal structure learning mechanism is designed to achieve efficient collaborative incremental learning between the server and high-quality, non-redundant new clients. This invention uses an improved clustering method and a multi-dimensional quality assessment strategy to select high-quality nodes from new clients. Among these high-quality clients, redundant new clients with historical causal structures similar to those on the server are identified to reduce redundant computation. Weighted federated aggregation learning is then performed on the selected new clients based on the historical causal structure, eliminating the need for full learning of both historical and new clients, effectively reducing communication overhead while maintaining learning accuracy.

The present invention provides a network data asset attribution identification method and system based on Bayesian causal reasoning, and relates to the field of data processing technology. The method includes: obtaining a network data asset data set; using heuristic rules, preferentially selecting asset features with high significance and high confidence as preliminary nodes of the Bayesian network; constructing an undirected preliminary Bayesian network based on the preliminary nodes; adding the remaining asset features as new nodes to the undirected preliminary Bayesian network to form an undirected intermediate Bayesian network; based on the point-to-point conditional causality of the node pairs composed of two parent nodes in the potential causal structure, determining the direction of the undirected edge, and converting the undirected intermediate Bayesian network into a directed final Bayesian network; obtaining the network data asset to be identified; based on the Bayesian theorem, calculating the posterior probability of each category label under a given feature combination; taking the category with the largest posterior probability as the final category of the network data asset to be identified, and outputting it.

To provide feedback for process improvement to increase the efficiency of on-site processes.SOLUTION: A feedback system using causal inference of the present invention includes: a data acquisition unit 121 that acquires sensor data in a manufacturing process 300; a causal inference unit 122 that performs causal inference using a causal structure in which the sensor data is a variable and a conditional probability between variables; an uncontrollable variable avoidance unit 123 that constructs an inference model by replacing an uncontrollable variable with a controllable variable when the result of the causal inference includes an uncontrollable variable; an optimization unit 125 that optimizes each controllable variable in the inference model; and an output unit 126 that feeds back the optimal value of each optimized controllable variable to the manufacturing process.SELECTED DRAWING: Figure 3

The application relates to a multi-modal data generalization learning method and system based on a causal invariant transformation. The method comprises the following steps: acquiring multi-modal data to construct a causal graph; designing a causal invariant transformation to simulate a causal relationship, keeping the causal features in the causal relationship unchanged, changing the non-causal features, and generating new training samples; fusing information of different modes; constructing a self-supervised learning framework, predicting labels of node pairs as a pre-training task, and learning feature representation and a causal structure of multi-modal data. By constructing and analyzing the causal graph, the causal invariant transformation is designed to simulate the causal relationship and generate new training samples, so that the model can learn the feature representation consistent with the causal structure, thereby maintaining stable performance on data of different distributions; the self-supervised learning framework is constructed, no additional labeling information is needed, the labels of node pairs are predicted as a pre-training task to learn the representation and the causal structure of the data, and the generalization ability of the model can be effectively improved.

The invention discloses an attribution analysis method and device, equipment, a storage medium and a computer program product, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the causal structure learning of a target data set, obtaining a target causal relationship in the target data set, and constructing a causal knowledge graph based on the target causal relationship; in response to a selection instruction of a main analysis index of a target user, querying a node set having causal association with the main analysis index from the causal knowledge graph, and determining a recommended association node based on causal strength; performing intervention effect calculation on the main analysis index based on the main analysis index and the recommended association node to generate anti-fact deduction result data; and performing multi-dimensional confidence assessment on the anti-fact deduction result data to obtain an attribution path credibility score. And through causal structure learning and multi-dimensional confidence assessment, the problem that attribution analysis depends on experience hypothesis is solved, and the analysis reliability is improved.

This invention discloses a method and apparatus for causal structure discovery based on expert knowledge correction, comprising: determining a performance factor dataset of a target protocol; obtaining an adjacency matrix of the target protocol obtained based on expert knowledge; training a preset autoencoder based on the performance factor dataset and the adjacency matrix, and determining a target causal graph based on the model parameters during training. This invention trains a preset autoencoder based on the adjacency matrix of the target protocol obtained based on expert knowledge and the performance factor dataset, and determines the target causal graph based on the model parameters during training, thereby obtaining the causal structure of the target protocol on performance influencing factors. This fully utilizes expert knowledge and improves the accuracy of generating the causal graph.

The application discloses a robot intelligent control method and system based on a causal mechanism, and relates to the technical field of intelligent robots, which comprises the following steps: inputting an operation task and an environment state of a target robot at a current time into a causal structure model to determine each candidate action for pushing a target object from a current time position to a next time position; inputting the candidate action into a graph recurrent network to obtain a predicted position of the target object under the candidate action at the next time; screening an execution action of the target robot at the next time according to the predicted position of the target object under each candidate action and a target position of the target object; and updating the predicted position corresponding to the execution action of the target robot at the next time to the current time position until the target object reaches the target position. The application can effectively improve the intelligent control capability of the robot by determining the action to be executed according to the predicted position of the target object under the execution action and the target position based on the causal structure model and the graph recurrent network.

The invention relates to the technical field of big data processing, in particular to a big data processing framework configuration tuning method and system based on causal reasoning. According to the method, multiple sample data sets are collected, multiple causal structure learning algorithms are adopted to analyze the sample data sets so as to construct a hierarchical causal structure model related to the data scale, then target scale data are taken into consideration, and a data scale sensing causal structure model is constructed. And a configuration parameter set adapted to the target new data is given, so that adaptive configuration adjustment and optimization aiming at the target scale are realized. According to the invention, the problem of poor tuning effect of the existing automatic configuration tuning method based on causal reasoning is solved.