Chemical accident cause analysis method based on text mining and causal inference

By employing text mining and causal inference methods, the problem of automated processing and customized analysis of unstructured safety texts within enterprises was solved. This enabled efficient, accurate, and reliable causal discovery for chemical accident cause analysis, provided target-specific prevention strategies, and improved the practicality of safety management.

CN121880545BActive Publication Date: 2026-06-26CHINA UNIV OF GEOSCIENCES (BEIJING)
2 Cites 0 Cited by

Patent Information

Application Number
CN202610352268.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-23
Publication Date
2026-06-26
Estimated Expiration
2046-03-23

AI Technical Summary

Technical Problem

Existing technologies cannot efficiently process unstructured security texts within enterprises, lack the ability to identify and customize the causal factors specific to each enterprise, and cannot quantify the expected effects of different intervention measures, resulting in unreasonable allocation of security resources and poor intervention effects.

Method used

Regular expressions are used for targeted semantic segment extraction, deep learning models are used for causal entity identification, density clustering algorithms and causal discovery algorithms are combined to construct the chemical causal structure, and structural equation modeling is used to infer causal strength and simulate counterfactual intervention to determine the optimal prevention strategy.

Benefits of technology

It has achieved automated processing of unstructured text, improved the objectivity and accuracy of causal analysis, provided target-specific and precise prevention strategies, promoted the shift of safety management from passive post-event attribution to proactive pre-event prevention, and improved the practicality of intervention guidance.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present application belongs to the technical field of chemical accident safety analysis, and discloses a chemical accident cause analysis method based on text mining and cause inference, which comprises: directional semantic segment extraction and cleaning treatment on the obtained chemical accident text; training of a pre-constructed deep learning model by using historical chemical accident data, cause entity recognition on the optimized cause description data; automatic aggregation of chemical cause semantic vectors into a cause theme set by using a density clustering algorithm; cause theme effect strength calculation on the cause theme set according to a pre-set multi-dimensional strength calculation index set, construction of a partial ancestor graph, and modification processing on the partial ancestor graph; cause and effect strength inference on the chemical cause and effect structure based on a structural equation model, counterfactual intervention simulation on the quantitative cause and effect network, and determination of the optimal prevention strategy. The present application can realize automatic processing of unstructured text and improve the automation and objectivity of cause analysis.
Need to check novelty before this filing date? Find Prior Art