8 results about "Causal link" patented technology

The application provides an enterprise resource planning system data flow conversion monitoring method, relates to the technical field of abnormal cause analysis in an enterprise resource planning system, and aims to solve problems such as difficult extraction of abnormal propagation causality, unexplainable traceability path, and low efficiency of quasi-real-time cause analysis in a multi-node complex business process. The core technical scheme includes the following steps: collecting and standardizing time series data of business key state indicators, establishing a local causal correlation cache by using a sliding window and a causal reasoning algorithm, recursively tracing back to an abnormal node as a terminal to form a preliminary causal path, screening high-confidence causal links through multi-dimensional time consistency evaluation, generating a simplified dynamic path by using a node semantic aggregation mechanism, and finally embedding the path in an operation and maintenance interface in the form of a visual trajectory to realize interactive cause analysis verification. The scheme improves the accuracy and explainability of abnormal cause analysis, realizes full-process automation and man-machine fusion optimization from data to root cause links, and significantly enhances the intelligent level of system abnormality handling.

The application relates to the field of artificial intelligence models and discloses a method, system, device and storage medium for evaluating content causality of a generative engine based on a structured causal model. The method comprises the following steps: obtaining a structured causal graph of a target industry field, user query information and to-be-evaluated content information; inputting the user query information and the to-be-evaluated content information into a causal enhancement model, performing causal link analysis on an original answer after the original answer is generated, extracting causal inference information and mapping the causal inference information to nodes of the structured causal graph, generating causal path data, quantifying causal necessity of the to-be-evaluated content information, obtaining a causal necessity index, a path causal contribution degree and a counterfactual influence index of the to-be-evaluated content information, and then outputting an evaluation result of the to-be-evaluated content information. The method provided in the application embodiment improves the stability of a model, thereby reducing the probability of redundant information in a model output result.

The application discloses a mine accident simulation training method and device based on VR technology and a medium, relates to the technical field of mine safety intelligent training, and comprises the following steps: collecting typical mine accident types, constructing accident causality, mapping accident elements in the accident causality with risk nodes in a mine semantic twin scene, and forming an accident causality structure; comparing accident perception delay data with a perception delay threshold, outputting a delay level judgment result, adjusting an accident evolution state according to the delay level judgment result, and outputting an adaptive accident evolution state; comprehensively analyzing the adaptive accident evolution state and a student operation track, reconstructing the accident causality structure, and obtaining a personalized accident retraining scene. The application realizes a VR safety training system by constructing a mine digital twin scene that integrates spatial semantics and accident causality logic, and improves the authenticity, teaching pertinence and training effectiveness of mine accident simulation.

This application provides a method, device, storage medium, and program product for analyzing the causal chain of power grid accidents. It relates to the field of power grid management technology. The method includes: acquiring text data of power grid accidents, including unstructured text; using the unstructured text as input to a pre-trained large language model to obtain multi-dimensional power accident information, wherein the power accident information includes key entities, accidents, and causal relationships; using key entities and accidents as nodes respectively, and generating edges connecting the nodes based on causal relationships to establish a structured knowledge network, which reflects the causal chain between key entities and accidents; and generating a causal chain report based on the structured knowledge network using entity relationship query and path analysis, which describes the causal chain and the structured knowledge network. This method aims to achieve accurate and efficient analysis of the causal chain of power grid accidents.

This invention discloses a method, equipment, and medium for simulating mine accidents using VR technology, relating to the field of intelligent mine safety training technology. The method includes: collecting typical mine accident types, constructing accident causal relationships, and mapping accident elements within these causal relationships to risk nodes in a semantic twin mine scene to form an accident causal relationship structure; comparing accident perception delay data with a perception delay threshold, outputting a delay level determination result, and adjusting the accident evolution state based on the delay level determination result to output an adaptive accident evolution state; and comprehensively analyzing the adaptive accident evolution state and the trainee's operational trajectory to reconstruct the accident causal relationship structure and obtain a personalized accident retraining scenario. This invention, by constructing a mine digital twin scene that integrates spatial semantics and accident causal logic, realizes a VR safety training system, improving the realism, relevance, and effectiveness of mine accident simulation.

The application provides a depression onset risk prediction method and system based on causal representation learning, and relates to the technical field of intelligent medical treatment. First, a multi-source implicit monitoring data set of a target object is obtained, which includes autonomic nervous function nonlinear micro features, intelligent terminal touch behavior micro features, environmental spatio-temporal big data, and non-depression related medical track data in an electronic health record. Then, the data set is subjected to causal variable level marking processing. Next, a causal variational autoencoder network is called to extract a causal latent representation vector set. Then, a causal link graph model is constructed based on the causal latent representation vector set and the causal variable level marking. Finally, causal weighted fusion processing is performed on real-time collected individual multi-source time series monitoring data according to the causal link graph model, and the depression super-early onset risk assessment model is input to generate a depression onset risk probability output result containing a prediction time window identifier. The application realizes super-early accurate prediction of the depression onset risk.

This invention discloses a causal analysis method for mining equipment failures based on a knowledge graph, relating to the field of industrial diagnostic technology. The method includes: collecting core data packets, establishing a unified correspondence and aligning them temporally to obtain time-series data, data quality identifiers, and operating condition labels; constructing an equipment failure knowledge graph based on the above data and generating a causal model template by combining structural data; weighting candidate causal relationships according to operating condition and quality weights to obtain operating condition parameters; filtering candidate edges based on operating condition activation consistency and cross-operating condition stability to obtain a structural causal model and causal relationship reliability; forming a confidence ranking through online inference; selecting executable diagnostic actions for verification when uncertain and outputting the root cause. By constraining the causal structure search space with allowed edge sets, prohibited edge sets, and mandatory edge sets, the method suppresses the spread of false causality and improves the robustness of multi-operating-condition inference. Simultaneously, it introduces causal relationship reliability weighting and a falsification loop to improve the deterministic location of the cause.

This application discloses an intelligent driving system optimization method based on automatic reconstruction of accident event chains and self-evolution of digital specimens. This method responds to safety event trigger signals, automatically collects and fuses multi-source data, and reconstructs a structured event chain based on a causal relationship model, achieving a qualitative leap from a "data pile" to a "storyline." Furthermore, it identifies key failure nodes through intelligent root cause analysis, parameterizing specific accidents into digital specimens containing core challenge parameters. New specimens are automatically added to a regression test case library for automated regression testing of new algorithm versions; only algorithms that pass all historical specimen tests are allowed for remote upgrades. This application, by constructing a fully automated closed loop of "perception-source tracing-specimenization-immunity," shortens the accident analysis cycle from "weeks" to "hours" and transforms each safety event into valuable knowledge for the system's "lifelong immunity," achieving continuous optimization of the intelligent driving system.