The probabilistic reasoning of hierarchical diagnosis decision-making

Aiming at the problems of information incompleteness, model complexity and conclusion inaccuracy existing in diagnosis decision-making reasoning under uncertain conditions, probability information and its unique attributes are introduced on the basis of ontology reasoning to realize the formal expression of uncertainty in various elements of diagnosis decision-making ontology, and the probability ontology is obtained. On this basis, Probabilistic Graphical Model (PGM)—Dynamical Uncertain Causality Graph (DUCG) is selected to implement complex uncertain causality in hierarchical diagnosis decision-making ontology and compact expression of effective probability reasoning. And the hierarchical diagnosis decision-making ontology based dynamic uncertain diagnosis decision-making causality diagram model is put forward, and its system structure is built. Combining with the ontology concepts, relations, attributes to build, simplify, split, and delete the causality diagram, the chain of reasoning, probability calculation and sorting under the uncertain conditions are realized.