50 results about "Causal structure" patented technology

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 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 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 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 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 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 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 relates to the technical field of causal relationship processing, in particular to a method for generating a general causal model in complex working condition data. According to the method, industrial equipment complex working condition multivariable time series data are collected and preprocessed, technologies such as a graph neural network and a Kupman operator are fused, and a universal causal model containing an encoder and a decoder is trained; new working conditions reuse the model to quickly generate an exclusive causal structure, and robustness measures such as a sliding window and fine tuning are matched. The limitation of one working condition and one model is broken, the generalization is high, and the modeling cost is low; the nonlinear time-varying causal relationship is accurately captured, the accuracy of causal relationship judgment is effectively improved, and reliable support is provided for intelligent operation and maintenance scenes such as fault root cause positioning.

This invention relates to the field of digital manufacturing and industrial information management technology, and discloses an information management system for a digital processing workshop, comprising: acquiring equipment abnormal event records to generate a subgraph of equipment fault propagation with observational evidence; calculating process similarity to screen source domain products; fusing source domain prior causal graphs to perform Bayesian causal structure learning to generate a posterior cross-process quality causal subgraph; calculating conditional Granger causal relationships to generate a set of cross-layer causal edges; assembling a two-layer causal graph; establishing an extended structured Bayesian network model; performing structured variational inference to output joint attribution results; and performing propagation path tracing to generate an attribution report with uncertainty quantification. This invention solves the technical problems in related technologies of digital processing workshop fault attribution, such as the inability to effectively integrate multi-source heterogeneous information from the equipment layer and process layer, the difficulty in completing cross-process causal structure learning under limited sample conditions, and the lack of uncertainty quantification in the attribution results.

The invention discloses a data causal structure learning method and system with group features, and the method comprises the steps: obtaining a currently arriving data feature group, and employing a conditional independence test to recognize a feature associated with each feature in the current data feature group, so as to construct a skeleton structure in the current data feature group; performing orientation on the skeleton structure in the current data feature group through V structure identification and using an orientation rule to form a causal skeleton structure in the current data feature group; fusing the causal skeleton structure in the current data feature group with an original causal skeleton structure in the current feature space, and constructing a causal skeleton structure diagram in the current state space; based on the causal skeleton structure diagram, identifying a causal diagram in a current feature space by using an orientation rule; according to the method, the contained causal structure relation can be found from the linear arbitrary distribution data with group characteristics, and the computer resource consumption is low.

This invention provides a computer program, an information processing apparatus, and an information processing method. The computer performs the following processing: acquiring observation data corresponding to multiple observation variables from an observation system; setting prior constraints based on the causal structure of the variable group in a reference system; and deriving the causal structure of the observation variable group in the observation system by performing learning with the prior constraints applied using the acquired observation data.

The invention discloses a directed ring causal structure discrimination and estimation method for network intelligent operation and maintenance, relates to the technical field of network intelligent operation and maintenance, and provides a solution based on high-order cumulant matrix analysis for causal discovery and effect estimation requirements of a feedback loop in a network system. A unified cumulant matrix construction principle is established, so that cumulant total orders of main diagonal elements and auxiliary diagonal elements of the matrix are the same; on the basis, the system firstly utilizes rank conditions of a forward three-order cumulant matrix and a reverse three-order cumulant matrix to realize loop detection; and after the existence of the feedback loop is confirmed, constructing a mixed third-order cumulant matrix, constructing a solvable equation about a causal coefficient by utilizing determinant properties, directly completing loop effect estimation, and realizing integration of loop discrimination and effect estimation. According to the method, a complex intra-ring causal effect estimation problem is converted into an algebraic problem which can be solved directly, and accurate causal support is provided for network performance optimization, resource scheduling and safety protection.

The application discloses a large language model guidance-based causal structure learning method and system, related schemes can make full use of high-quality and easily accessible prior knowledge provided by a large language model across multiple fields, thereby improving the practicality of causal relationship discovery in actual scenarios, introducing prior knowledge provided by the large language model in causal structure learning, simplifying the search process, and more accurately discovering unknown causal relationships under limited data conditions, thereby providing more accurate and efficient support for data-driven decision-making and prediction; and since the large language model can provide causal prior knowledge across fields, the application has wide application prospects and can realize reliable causal relationship discovery in different fields.

A method includes receiving initial conditions for a traffic scenario including a plurality of interacting agents, the initial conditions defining states of the plurality of agents, identifying a causal structure among the plurality of agents based on the states of the plurality of agents, and ranking the plurality of agents based on the identified causal structure to determine a subset of key agents being most influential with respect to a controllability objective. For each agent of the plurality of agents, the method includes generating a future trajectory using a reverse sampling process of a diffusion model, and guiding the reverse sampling process by selectively applying a gradient of the controllability objective only to the determined subset of key agents.

The invention discloses a multimodal transport-oriented freight demand causal relationship graph construction method, and relates to the field of intelligent logistics, and the method comprises the steps: carrying out the collection and causal perception fusion processing of multi-source heterogeneous data from a multimodal transport service, and obtaining a time-space aggregation data table; based on the space-time aggregation data table, performing preliminary discovery of a causal structure by adopting a causal discovery algorithm fusing prior knowledge in the multimodal transport field to obtain a preliminary causal directed acyclic graph; and outputting the preliminary causal directed acyclic graph to a spatial-temporal heterogeneity causal effect estimation model based on a graph neural network and a super network, and calculating a personalized causal effect changing along with a space unit and a time context to obtain a spatial-temporal heterogeneity causal graph. According to the method, the causal atlas which can quantify causal intensity, reflect space-time heterogeneity and embed market game behaviors and has dynamic deduction capability is constructed, and decision support is provided for multimodal transport strategy optimization through visual integration.

An interpretable solution generation method based on causal reasoning graphs and large language models is proposed, characterized by: Step 1: generating solution tasks according to requirements; Step 2: introducing a causal model to more accurately characterize the decision-making environment; Step 3: adding causally identifiable optimal interventions to generate solutions. This application proposes an interpretable solution generation method based on causal reasoning graphs and large language models. This method combines the structured causal reasoning capabilities of causal reasoning graphs with the natural language generation capabilities of large language models. Reasoning graphs, with events as nodes and relationships between events as edges, can describe the evolutionary patterns of dynamic processes. To support causal reasoning, this application further introduces causal structures to construct causal reasoning graphs.

The application provides a kind of based on intelligent operation and maintenance's causal analysis method, device, equipment and medium, receive the original telemetry data of each cloud platform, the directed causal dependence relationship between variables is identified based on the original telemetry data, generate the graph structure information of local causal structure, and the graph structure information is differentially private protection processing, to output the local causal structure information after adding noise;Based on the aggregation weight of each cloud platform, the local causal structure information is aggregated, and the consensus causal graph is obtained, and the causal strength parameter is determined based on the consensus causal graph;When detecting system exception, based on the consensus causal graph and the causal strength parameter, the root cause score of each entity in consensus causal graph is calculated, the main root cause entity is identified based on the root cause score of each entity ordering, and a causal analysis report is generated according to the main root cause entity. Through the method and device, the accuracy of abnormal attribution in intelligent operation and maintenance is significantly improved.

The invention discloses an intelligent AI-based occupational planning method and system, and particularly relates to the technical field of electric digital data processing. The method is used for solving the problem of insufficient planning suggestion accuracy caused by difficulty in distinguishing real causal correlation and false statistical correlation due to a large number of hybrid variables in student behavior data when occupational planning is carried out by using artificial intelligence at present. Historical behavior sequence data of a target college student and a corresponding historical occupational development result are obtained and processed to form a feature-result pair, cross-sequence consistency verification analysis is performed based on the feature-result pair, and causal verification information for constraining a false causal relationship is generated. The information is used as a constraint to construct a causal structure diagram, causal contribution dispersion of each student is calculated based on the causal structure diagram, clustering layering is performed on a student group according to the causal contribution dispersion, a causal effect of a characteristic variable and an occupational development result is estimated for each layered sub-group, and an occupational planning suggestion is generated based on the effect.

A method includes receiving an initial condition of a traffic scene including a plurality of interactive agents, the initial condition defining a state of the plurality of agents; identifying a causal structure between the plurality of agents based on the states of the plurality of agents; and ranking the plurality of agents based on the identified causal structure to determine a subset of key agents that have the greatest impact on the controllability goal. For each agent of the plurality of agents, the method includes generating a future trajectory using a reverse sampling process of a diffusion model, and directing the reverse sampling process by selectively applying a gradient of a controllability target only to a determined subset of key agents.