关键词: 仿真技术, D-S(dempster-shafer)证据理论, 船舶, 数据融合, 火灾预测

Abstract:

A multi-source information fusion approach based on the dempster-shafer (D-S) evidence theory with a fuzzy reward-penalty mechanism was proposed to address the issues of underreporting and false reporting in the early prediction of ship fires. PyroSim was utilized to construct a ship's laboratory model for fire simulation. Variations in carbon monoxide, temperature, and smoke concentration were recorded for data acquisition, followed by the application of a sigmf function for membership assignment. By leveraging the classical D-S theory, a reward-penalty mechanism was applied in weighted evidence fusion. Reward-penalty factors were utilized to differentiate various basic probability assignments, with unified belief assignmentsbeing incorporated to mitigate the risk of failure due to high conflicts among evidence, thereby enhancing the convergence of evidence fusion. The research findings demonstrate that the proposed method can increase the prediction accuracy from 57.3% to 95.41% at the same time point for fire prediction, effectively reducing the imprecision of fire prediction.

Key words: simulation technology, dempster-shafer(D-S) evidence theory, ship, data fusion, fire prediction