Modeling and Verification of Intelligent Requirement Engineering System under the Background of Human Machine Object Fusion

In order to improve the safety, reliability and interpretability of HCPS systems, we aimed to solve the problem of non-functional quality requirement verification in intelligent requirement engineering in HCPS systems. We selected a causal reasoning-based method for research, expanded the existing problem framework, and adopted the basic HCPS theory based on causal reasoning and argumentation. Then, we used the causal reasoning problem framework and reasoning rules constructed in the early stage to reason and analyze the behavior of the HCPS system. Finally, we applied the causal reasoning-based method to the problem domain knowledge modeling, completion and non-functional requirement verification of the HCPS system. Under the coarse-grained explanation, the explanation clarity of the system decision process explanation (explanation requirement D) reached 89%. This study not only improved the safety, reliability and interpretability of the HCPS system, but also provided effective technical support for the design and verification of the system.