## 3 Empirical Research  
### Selection of the Indicator System  
In this paper, the innovation and R&D process of high-tech manufacturing is regarded as a complex two-stage process, in which the first stage is the technology R&D stage, and the second stage is the result transformation stage. The two-stage input structure discussed in this paper includes both shared inputs and independent inputs of each stage. Therefore, when considering the shared inputs, this paper selects the full-time equivalent of R&D personnel as the shared personnel input, and the internal expenditure of R&D funds as the shared capital input; when considering the independent inputs, it selects the expenditure of new product development funds and the annual average number of practitioners as the independent input elements of the result transformation stage. Meanwhile, considering the availability of data, the number of R&D projects and the number of effective invention patents are taken as the linking variables between the technology R&D stage and the result transformation stage, and the sales revenue of new products and the main business revenue are taken as the output variables of the result transformation stage. In addition, referring to the study of [PERSON] et al. [28], the stock of research funding is taken as the carry-over variable of the technology R&D stage, and the stock of development funding is taken as the carry-over variable of the result transformation stage.  
Both stages of the innovation process of the high-tech manufacturing industry will have a certain time lag, so in the study of the innovation efficiency of the high-tech manufacturing industry, a certain lag treatment of the corresponding variables needs to be carried out. And there is not yet a unified standard for the lag period of innovation development. [PERSON] and [PERSON] believe that the use of time lag has almost no effect on the estimation of the innovation efficiency [29]. Preliminary tests were conducted through a series of correlation and regression analyses, which showed that a 2-year lag versus a 1-year lag in the innovation R&D process in the high-tech manufacturing industry had little effect on both innovation efficiency estimates. Therefore, this paper specifies a 2-year lag for the technology R&D stage and a 1-year lag for the result transformation stage.  
Based on this, the evaluation indices of innovation and R&D efficiency of the high-tech manufacturing industry, as constructed in this paper, are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Segmentation by Stage** & **Form** & **Indicator** \\ \hline \multirow{4}{*}{Technology research stage} & Shared input indicator & Full-time equivalent of R\&D personnel (t) \\  & & Internal expenditure on R\&D funds (t) \\  & & Number of R\&D projects (t + 2) \\  & Link output indicator & Number of active patents (t + 2) \\  & Carry-over output indicator & Research funding stock (t + 2) \\  & Independent input indicator & Expenditures for new product development (t + 2) \\  & Average annual number of practitioners (t + 2) \\ \multirow{2}{*}{Achievement transformation stage} & Carry-over output indicator & Stock of development funds (t + 3) \\  & Final output indicator & Revenue from sales of new products (t + 3) \\ \end{tabular}
\end{table}
Table 1: Evaluation indicators of innovation efficiency of the high-tech manufacturing industry.  
The dynamic network evaluation structure of two-stage shared inputs for innovative R&D activities in high-tech manufacturing industries is shown in Figure 2.