74 results about "Causal maps" patented technology

The invention provides a rock and soil construction quality monitoring and diagnosing method, which comprises the following steps of: modeling historical engineering events and expert rules, constructing an event causal atlas prototype containing weights, and realizing event sequence feature extraction and real-time causal atlas dynamic updating in combination with on-site multi-modal sensing data and construction logs; neural symbol reasoning and tensor completion technologies are adopted to predict a novel causal relationship, and a causal atlas structure is perfected through space, time and logic consistency verification; a multi-layer risk early warning mechanism is set, a causal map local risk assessment and event chain propagation are combined, spatial positioning early warning signals are generated in a grading manner, closed-loop backtracking optimization is supported, causal reasoning accuracy and early warning efficiency are improved, construction process risk identification and dynamic early warning can be realized, and the engineering safety management level is improved.

The invention relates to the technical field of atmospheric environment prediction, and discloses an atmospheric environment multi-parameter intelligent prediction method based on causal VAE and CNN-LSTM. The method comprises the steps of collecting multi-parameter historical data of an atmospheric environment to form an atmospheric parameter sequence; sequence causal features are extracted, a causal graph structure is constructed, a causal VAE model is trained according to the causal graph structure, and potential causal characterization is generated. And extracting spatial features represented by potential causality through a CNN module, capturing time-dependent features of the spatial features through an LSTM module, fusing the two types of features to obtain a fused feature sequence, and outputting a multi-parameter prediction result through a prediction network. And dynamically adjusting hidden variable distribution parameters of the causal VAE according to the prediction error, updating a causal graph structure according to the adjusted parameters, and re-predicting a subsequent atmospheric parameter sequence by using the updated causal graph. The method can improve prediction precision and generalization ability, and enhance model interpretability and dynamic adaptability.

The invention discloses a deep reinforcement learning optimization method for injection molding process parameters, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: constructing a dynamic causal graph network through information entropy flow analysis and transfer entropy calculation, and revealing a causal relationship and time delay characteristics among process parameters; manifold learning is adopted to map a high-dimensional parameter space to a low-dimensional manifold, and Riemannian metric guide optimization search is constructed based on the quality gradient; generating enhanced state representation fusing causal association and manifold geometric information; identifying a production element state and selecting a corresponding optimization strategy; a geodesic line is planned in a manifold space to obtain an optimal parameter adjustment path; historical experience is utilized through memory retrieval and case adaptation; cross-task knowledge migration is realized; adopting a depth deterministic strategy gradient algorithm to optimize the decision; and online learning is realized through elastic weight consolidation. According to the method, the problems of black box decision, slow convergence, difficulty in knowledge reuse and the like in the prior art are solved, the optimization efficiency and the interpretability are improved, and the method has the capability of quickly adapting to new tasks.

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 relates to the technical field of target behavior prediction systems based on causal inference and multi-task learning, and particularly discloses a target behavior prediction system based on causal inference and multi-task learning. The system comprises a central coordination server and a plurality of participant clients, constructs a global causal graph through a federated causal discovery algorithm in a collaborative manner, determines a causal feature subset of each prediction task, and carries out multi-task model training through a federated average algorithm under the constraint. In the process, differential privacy and homomorphic encryption technologies are comprehensively applied to protect data privacy. According to the method, more accurate causal discovery and more reliable prediction model training can be realized on the premise of protecting data privacy of all parties.

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 causal decoupling method and device based on multi-scale noise and adversarial supervision, and the method comprises the steps: carrying out the simulation of the causal relationship of variables in a causal graph, so as to generate observation image data, and constructing a training set and a test set according to the observation image data, the corresponding causal label information and the causal graph; constructing a causal decoupling model, and performing adversarial supervision training under multi-scale noise by using the training set; and obtaining anti-fact intervention data by using the test set and the trained causal graph matrix and utilizing the trained observation data coding module and observation data decoding module. According to the method, an auto-encoder and causal acyclic constraints are fully combined, the discrimination module is trained under multi-scale noise, and high-quality adversarial supervision is performed, so that the model representation learning ability is improved, the representation understanding of the model on data with causal relationships is enhanced, the accuracy of implicit causal network prediction is improved, and the prediction efficiency is improved. And the causal decoupling accuracy is improved.

The invention belongs to the technical field of natural language processing and artificial intelligence, and relates to a biomedicine named entity recognition method based on causal graph guided anti-fact analysis, which comprises the specific steps of causal graph construction, false node anti-fact analysis, false link anti-fact analysis, consistency constraint and model training and optimization. According to the method, a dual anti-fact analysis mechanism based on syntactic analysis and adversarial disturbance is introduced into large-scale language model training, so that false factors in input features can be effectively identified and intervened. Therefore, the model can focus on core features with real causal association in an entity identification task, so that the robustness and the cross-domain generalization ability of the model are remarkably enhanced. Experimental results prove that the Micro-F1 scores on a plurality of biomedical named entity recognition reference data sets are all advanced to the prior art.

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 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 question and answer method and system based on large model causal graph discovery and causal graph enhanced reasoning, and belongs to the technical field of natural language processing and artificial intelligence. The method comprises the following steps: firstly, carrying out hierarchical expansion from an initial root node by adopting a breadth-first search strategy, and efficiently constructing a reliable causal graph conforming to directed acyclic graph constraints with linear complexity in combination with real-time loop detection; in the question and answer stage, key entities in user questions are analyzed, and causal paths connecting the key entities are retrieved in a causal graph; and taking the retrieved structured causal path as a constraint condition to be injected into a decoding process of a large-scale language model, and generating a natural language answer which is strict in logic and can trace reasoning steps. According to the method, the resource consumption of large-scale causal discovery is remarkably reduced, the accuracy and interpretability of the answers of the questions and answers are effectively improved, and technical support is provided for medical treatment, finance and other scenes needing high-reliability reasoning.

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 invention discloses a method, a product and equipment for generating a mountain torrent disaster dynamic plan based on causal inference, and belongs to the technical field of mountain torrent disaster monitoring. The method comprises the following steps: S1, acquiring multi-source data, and extracting causal features of the multi-source data; during extraction, dual robust data deviation elimination is realized based on a tendency scoring model and a result regression model; s2, performing dynamic causal graph modeling based on the extracted causal features and a historical disaster data set, and updating the weight to obtain a causal graph with a time sequence weight; s3, determining a current disaster situation state according to the causal diagram information, and performing anti-fact plan generation in combination with a preset intervention measure set to obtain an optimal plan set; and S4, performing multi-department collaborative execution based on the optimal plan set. According to the method, the pain point problems of passive response, lack of causal support and cross-department collaboration low efficiency of a traditional mountain torrent plan can be solved.

The new energy ship fault causal relationship construction method provided by the invention comprises the following steps: when a fault occurs, fusing multi-modal heterogeneous data and screening to obtain a core feature set; obtaining an environment invariant feature matrix through invariant risk minimization learning, evaluating causal edge strength among variable features based on mutual information to obtain a causal edge strength matrix, and mining a core fault causal skeleton in combination with a causal edge strength threshold and a condition independence test; based on the skeleton and the hierarchical node system, constructing an initial hierarchical fault causal graph, fusing a causal edge strength matrix to determine an initial causal edge weight, and dynamically updating by using a meta-learning model to obtain a target hierarchical fault causal graph; and after the target hierarchical fault causal graph is corrected through an anti-fact sample, a reasoning path is optimized, and then the fault causal relationship of the new energy ship is obtained through structured reasoning and combined with a large language model to mine an implicit causal relationship. Therefore, stable and self-adaptive fault causal relationship mining under a complex dynamic working condition is realized.

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 discloses a causal knowledge graph construction and question-answering system based on retrieval enhancement generation and a large language model. The system comprises a multi-source heterogeneous knowledge base construction module, a retrieval enhancement generation module, a named entity recognition and causal triple extraction module and a knowledge fusion reasoning module, according to the method, a high-quality named entity annotation data set and a causal data set are constructed, and normalization and integrity of input knowledge are guaranteed; a mixed retrieval strategy (keyword + vector + sparse embedding) is provided, and the evidence coverage rate and recall precision are improved. Under RAG driving, LLM is combined, two rounds of causal triple extraction are achieved, and the causal relationship coverage degree and the direction judgment confidence degree are improved; a conflict detection and atlas fusion mechanism is designed to ensure the unity and consistency of new and old causal knowledge; a question answering system based on a causal atlas is also established, multi-hop causal reasoning is supported, and answers with controllable credibility and explainable are output.

The application provides a machine learning-based financial account allocation and anomaly detection method and system, relates to the technical field of machine learning, and comprises the following steps: a causal diagram model is constructed, a conditional random field is used to generate a time sequence attention weight matrix to perform weighted fusion, and a difference reason is attributed; a reinforcement learning method is used to process a difference feature vector, a processing strategy is generated and optimized, and finally a difference processing scheme is output. The application can improve the account accuracy, automatically find abnormal transactions, reduce the manual processing cost, and effectively prevent financial risks.

The invention relates to a system (100) for generating real-time causal reasoning insights in real time. The system (100) comprises a global causal graph creation module (101), a natural language query processing module (102), an information extraction module (103), a query-centric subgraph generation module (104), and an adaptive causal reasoning engine (105). The queries from users are processed to identify relevant causal relationships in the global causal graph. The adaptive causal reasoning engine (105) refines the insights iteratively based on user feedback, improving accuracy. The system (100) converts technical insights into structured narratives. A visualization module presents causal relationships through graphs and charts. The feedback mechanism allows users to correct errors and adjust outputs, facilitating user-driven data analysis and causal reasoning, delivering actionable insights for a variety of applications.

The application provides a causal discovery method and device fusing field knowledge of a large language model and a flow model, and relates to the technical fields of artificial intelligence and data mining. The method comprises the following steps: acquiring observation data and variable semantic information of a system to be analyzed; determining a causal topological order between variables by using a large language model; constructing a mask affine autoregressive flow model based on the causal topological order, and training the model by using the observation data; calculating a Jacobian matrix of the flow model with respect to input data, constructing a statistic based on the Jacobian matrix, and performing hypothesis testing; pruning according to a test result to obtain a candidate causal graph; inputting the candidate causal graph into the large language model, and performing secondary verification and pruning on candidate connections by using the variable semantic information to obtain a final sparse causal graph. The application introduces prior knowledge by using semantic information of the large language model, and fits data by using a more flexible function form of the flow model, thereby significantly improving the accuracy and robustness of causal discovery.

This application discloses a data prediction method, apparatus, device, storage medium, and computer program product, relating to the fields of artificial intelligence and data processing technology. Embodiments of this application can be applied to fields such as mapping and transportation. The method includes: acquiring a universal causal graph to characterize causal knowledge prevalent in different regions, whereby the causal knowledge characterizes causal relationships between attributes of different regions; based on the universal causal graph and observation data of a target region, acquiring the full range of regional attributes corresponding to each region in the target region, including unobserved regional attributes; and based on the full range of regional attributes corresponding to each region in the target region, acquiring predicted values ​​for object flows between regions in the target region. This application solves the problem of inaccurate object flow prediction in some regions due to a lack of observation data, thus improving the accuracy of object flow prediction.

This invention discloses a verifiable application attack detection method based on artificial intelligence causal inference, including data collection and structuring, causal graph construction, intervention effect calculation, abnormal causal determination, verifiable evidence chain generation, detection result output, and false positive adaptive correction steps. By constructing a structured causal graph model between attack behavior and abnormal system states, this invention can effectively distinguish between "spurious correlations" and "true causality," significantly reducing the false positive rate caused by business fluctuations or system changes. The verifiable mechanism designed in this invention can generate a complete and tamper-proof causal evidence chain from the original attack payload to the final abnormal state for each detection conclusion, making the detection results not only accurate but also transparent, auditable, and reproducible. This greatly improves the judgment efficiency of security operations teams and provides a reliable technical foundation for security forensics scenarios based on blockchain or third-party arbitration.

This application belongs to the field of mine radiation protection and safety monitoring technology, specifically relating to a method and system for locating radon anomaly sources in mines based on causal graphs. The method includes: standardizing the original data matrix to obtain a feature matrix; obtaining a directed acyclic graph (DAG) based on the feature matrix; using the DAG and the set of parent nodes, constructing marginal distributions for the root node and conditional distribution estimators for the child nodes to obtain a global distribution estimator; calculating the intervention distribution of the feature subset based on the global distribution estimator; calculating the causal Shapley value based on the intervention distribution; retraining the anomaly prediction model after feature filtering to obtain the final anomaly source model; and inputting actual radon concentration time-series data into the final anomaly source model to obtain the anomaly source location. This application has the effect of accurately distinguishing anomaly sources at different locations and achieving globally convergent radon anomaly source location.

The application discloses a kind of based on diffusion model and causal optimization's graph neural network missing data filling method, belong to data filling technical field, it includes that data table is divided into multiple time series, and respectively input mask perception diffusion module carries out missing data filling, obtains initial denoising result, and input time lag convolution's dynamic causal learning machine obtains causal graph;Initial denoising result is as each node input of graph neural network, using causal graph guides graph neural network to carry out cross feature denoising, obtains reconstruction value;Using the numerical value in the reconstruction value of initial denoising result fills in the missing value in time series, obtains new time series;Whether graph neural network converges is judged, if yes, then splice new time series and obtain data table after data filling, otherwise, new time series is as initial denoising result input dynamic causal learning machine.This scheme can make full use of cross feature information and make up the lack of filling result in interpretability and consistency.

The invention discloses a multi-round question answering system and method based on dynamic knowledge completion. The system comprises a real-time knowledge completion module, a multi-round dialogue state management module and a self-adaptive optimization engine. The method comprises the steps that a dynamic causal graph is constructed to replace a traditional semantic matching technology, a logic association chain between events is established to achieve accurate decision, and dialogue strategy dynamic optimization is achieved through field constraint driven reinforcement learning based on deep deterministic strategy gradient framework design. And realizing dynamic optimization through a collaborative mechanism of online learning and knowledge base updating, wherein the dynamic optimization comprises dynamic loss function design and a knowledge base updating strategy. According to the method, the problem of context splitting in multiple rounds of dialogues is effectively solved, and the response logic continuity in a complex scene is remarkably improved; the concept confusion and wrong reasoning risks in professional instruction analysis are greatly reduced; and the system response efficiency and the decision reliability are integrally improved.

The application discloses an enterprise fund income and expenditure prediction method based on an AI large model and multi-modal interaction, and particularly relates to the field of enterprise asset prediction, and is used for solving the problem that the existing fund prediction method relies on a single data source and lacks causal explainability. The method collects text, image and time sequence multi-modal data related to enterprise fund income and expenditure, constructs a hierarchical causal diagram containing an upper layer and a lower layer, aligns features in a unified semantic space by using a multi-modal Transformer to generate modal feature vectors, dynamically adjusts the weight of a modal node in the hierarchical causal diagram based on reinforcement learning, extracts short-term and long-term dependencies by combining a time sequence causal convolution and a memory network, generates a time sequence enhanced multi-modal representation, and infers the fund income and expenditure change trajectory under different external event scenarios by using counterfactual reasoning and a Bayesian network model, so that long-period and scenario-based prediction of the enterprise fund operation state is realized, and the prediction accuracy and explainability are improved.

The invention provides a brand vision self-adaptive generation and optimization method driven by multi-modal data, relates to the technical field of computer data processing, and aims to identify historical intervention events from historical multi-modal data and construct a dynamic causal map as a unique control center according to the historical intervention events and the historical intervention events through a causal discovery algorithm. A complete closed loop of sensing, decision making, execution and learning is formed, in the generation stage, an adjustment target obtained through causal reasoning is converted into a gradient signal in a submerged space to conduct optimization iteration of the sampling process, it is ensured that output content is accurately matched with a scene target, and in the optimization stage, the output content is accurately matched with the scene target. The dynamic causal atlas is calibrated and updated by collecting market feedback data of brand visual content, so that causal relationships and weights in the dynamic causal atlas are automatically evolved and iterated, consistency and adaptability are dynamically balanced, brand vision has continuous learning from market feedback, and the dynamic causal atlas can be automatically analyzed and updated. And the generated brand visual content can more accurately meet the requirements of multiple parties.

This invention discloses a financial risk intelligent early warning method and system based on deep neural networks. It constructs a four-layer time-series hierarchical financial causal ontology, adding hierarchical flow constraints and prior causal constraints to the traditional NOTEARS algorithm, and dynamically updates the causal model through a sliding time window. An end-to-end financial risk early warning model with causal gating constraints is built, embedding a causal mask matrix into model feature extraction, designing a dual-constraint loss function, and embedding causal consistency and intervention invariance constraints into the entire model training process to ensure the causal reliability of the model's prediction results. A time-series dynamic counterfactual reasoning engine is constructed, building a time-series hierarchical causal structure equation model based on a dynamic causal graph, designing a four-step counterfactual reasoning process adapted to the financial cycle, and outputting Pareto-optimal intervention schemes through multi-objective reinforcement learning. This solves the problem that existing counterfactual analysis results are detached from reality and cannot be implemented.

The application discloses a kind of robust causal relationship learning method, system, equipment and storage medium, large-scale variable set can be effectively handled, and when variable set scale is larger, the problem is decomposed into multiple small-scale problems and is handled, improve the efficiency of processing large-scale variable set;Meanwhile, the application has good scalability, can be used with any causal algorithm, so that the application can adapt to a variety of different application scenarios and needs;And, the application provides a new way to solve complex causal inference problem, the idea of divide and conquer and the concept of causal cut provide a new perspective and tool for subsequent causal inference research;In addition, the directed causal graph finally obtained by the application has high accuracy, and can improve the effect of the application field.

The invention provides a causal analysis method, system and device, electronic equipment and a storage medium. The method comprises the steps that target record information of a to-be-analyzed object and a target causal graph are acquired; the target causal graph is a causal graph obtained by optimizing an initial causal graph generated based on world knowledge through a gradient-based numerical method; the initial causal graph comprises a confusion variable set generated based on world knowledge; and according to the target record information and the target causal graph, generating causal analysis information for the target variable of the to-be-analyzed object. The causal discovery is guided through world knowledge, confusion variables (such as air temperature and seasons) can be automatically identified, and a causal structure is finely corrected by fusing a numerical method, so that the problems that a traditional method depends on manual variable presetting and is low in multi-modal data processing efficiency are solved.