3 results about "Consequence analysis" patented technology

This invention provides a method and system for pre-analyzing the impact of public figures' statements based on multi-agent intelligence. The method includes: first, a social background generation module retrieves background information and related social issues of the public figure in parallel, constructing a semantic environment that closely resembles reality. Then, the core multi-agent discussion module is activated, driving multiple agents to conduct multiple rounds of simulated discussions. This process is not simple text analysis, but a dynamic simulation of the clash of viewpoints, the evolution of positions, and the fermentation of public opinion in real society. Finally, the consequence analysis module outputs a structured, multi-dimensional risk report based on the simulation results. This transforms an invisible, future public opinion risk into a quantifiable and analyzable "sandbox simulation" result through technological means, achieving a fundamental shift from "passive firefighting" to "proactive fire prevention." Its technological superiority is unmatched by existing post-event negative situation analysis tools.

The application provides a natural gas pipeline third-party damage accident early warning method based on a dynamic Bayesian network. The method comprises the following steps: first, third-party damage accident factor identification; second, risk accident scene building; third, accident consequence analysis, an ESD is used to build a sequence diagram about the natural gas pipeline failure consequences caused by the third-party damage; fourth, node probability calculation; fifth, DBN dynamic quantitative modeling coupled with the ESD and the FT; sixth, third-party accident probability dynamic analysis based on the DBN model; and seventh, risk control measures are summarized according to the failure probability analysis results. The method can obtain the pipeline failure risk probability by calling historical data, calculating the occurrence probability of each factor by using a fuzzy comprehensive evaluation method, and reasoning the input data information of the Bayesian network as the data information, and the probability information is combined with the accident consequence analysis method to perform early warning.