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During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research.

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At present, the commonly used hot rolling model is only applicable to the static rolling process. However, to study the dynamic rolling process, a dynamic rolling model with roll vertical movement velocity parameters is required. In this study, the influence of the vertical movement velocity of the rolls on the rolling process is considered, and a dynamic rolling process model is proposed when the roll gap is reduced during the hot rolling dynamic rolling process. Mathematically, the model is based on the upper limit method. The model considers the influence of the dynamic rolling process on the length of the deformation zone, establishes a dynamic velocity field model and an average deformation rate model, and then solves the total power of the rolling process. Finally, the dynamic rolling force equation is given. Compared with the experimental results, the dynamic rolling model in this paper has high accuracy with an average error of 4%. In addition, the influence of roll vertical velocity on rolling parameters is discussed, which provides a basis for the study of the dynamic rolling process.