## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System
### Neural Network Model Based on the Fuzzy-Rough Set
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location** & **Nci** & **X** & **Y** & **Z** & **F** \\ \hline \multirow{8}{*}{R-Station} & a & 35/100 & 15/100 & 22/100 & 32/100 \\  & b & 23/100 & 21/100 & 15/100 & 31/100 \\  & c & 26/100 & 12/100 & 16/100 & 19/100 \\  & d & 3/100 & 18/100 & 25/100 & 21/100 \\  & e & 15/100 & 12/100 & 19/100 & 6/100 \\  & f & 2/100 & 5/100 & 2/100 & 1/100 \\  & g & 1/100 & 2/100 & 3/100 & 2/100 \\  & h & 3/100 & 7/100 & 15/100 & 4/100 \\  & i & 15/100 & 10/100 & 34/100 & 23/100 \\ \hline \multirow{8}{*}{Y-Station} & a & 32/100 & 12/100 & 19/100 & 29/100 \\  & b & 20/100 & 18/100 & 13/100 & 23/100 \\  & c & 21/100 & 17/100 & 11/100 & 24/100 \\  & d & 15/100 & 18/100 & 25/100 & 21/100 \\  & e & 15/100 & 12/100 & 19/100 & 6/100 \\  & f & 12/100 & 18/100 & 16/100 & 21/100 \\  & g & 25/100 & 29/100 & 32/100 & 25/100 \\  & h & 10/100 & 2/100 & 8/100 & 13/100 \\  & i & 0/100 & 0/100 & 0/100 & 0/100 \\ \hline \multirow{8}{*}{H-Station} & a & 35/100 & 15/100 & 22/100 & 32/100 \\  & b & 23/100 & 21/100 & 15/100 & 31/100 \\  & c & 26/100 & 12/100 & 16/100 & 19/100 \\ \cline{1-1}  & d & 3/100 & 18/100 & 25/100 & 21/100 \\ \cline{1-1}  & e & 15/100 & 12/100 & 19/100 & 6/100 \\ \cline{1-1}  & f & 4/100 & 9/100 & 1/100 & 0/100 \\ \cline{1-1}  & g & 5/100 & 6/100 & 9/100 & 1/100 \\ \cline{1-1}  & h & 21/100 & 15/100 & 18/100 & 28/100 \\ \cline{1-1}  & i & 0/100 & 0/100 & 0/100 & 0/100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation sheet for coordination rate of each factor. This paper describes the surrounding rock integrity \(x_{1}\) as three fuzzy nodes, which are fewer fractures, ordinary fractures, and fragmentation. Furthermore, based on the three nodes, the membership functions of rock integrity \(x_{1}\) after fuzzification were established. \[\mu_{l}(x_{1})= \begin{cases}1&x_{1}=0\\ \left(0.55-x_{1}\right)/0.55&0<x_{1}<0.55\\ 0&x_{1}\geq 0.55\end{cases}  \tag{7}\]  
\[\mu_{o}(x_{1})= \begin{cases}0&x_{1}\geq 0.75\\ \left(0.75-x_{1}\right)/0.2&0.55\leq x_{1}<0.75\\ \left(x_{1}-0.35\right)/0.2&0.35\leq x_{1}<0.55\\ 0&x_{1}<0.35\end{cases}  \tag{8}\]  
\[\mu_{f}(x_{1})= \begin{cases}1&x_{1}=1\\ \left(0.55-x_{1}\right)/0.45&0.55<x_{1}<1\\ 0&x_{1}\leq 0.55\end{cases}  \tag{9}\]  
The fuzzification of the relative position between the measuring point and the face  
Regarding the center point of the excavated surface as the coordinate origin, the polar axis is established in the direction of the tunnel face. The relative position \(x_{2}\) between the tunnel face and the measuring point can be represented by the angle value of the measuring  
Figure 2: The topology of fuzzy neural network in different sites. point and the palm surface. The relative position between the measuring point and the palm surface can be described as the front area of the tunnel face and back area of the tunnel face. The membership function of the relative position of the palm surface and measuring point can be established. \[\mu_{f}(x_{2})= \begin{cases}0&x_{2}=0^{\circ}\\ 1&0^{\circ}<x_{2}<180^{\circ}\\ 0&180^{\circ}\leq x_{2}\leq 360^{\circ}\\ \end{cases}  \tag{10}\]  
\[\mu_{b}(x_{2})= \begin{cases}1&x_{2}=0^{\circ}\\ 0&0^{\circ}<x_{2}<180^{\circ}\\ 1&180^{\circ}\leq x_{2}\leq 360^{\circ}\\ \end{cases}  \tag{11}\]  
(3) The fuzzification of the relative height of building floors  
The relative ratio of high-rise buildings floors (greater than 15 floors) is introduced as the number of references to evaluate the relative height of floors to represent the relative position of \(n\)-story buildings. \[x_{3}=n/n_{total} \tag{12}\]  
where \(n\) is the floor where the measurement point is located and \(n_{total}\) is the number of floors of the building. The factor can be expressed in the fuzzy language of the bottom layer, middle layer, and high layer, and the membership function is established as follows:  
\[\mu_{L}(x_{3})= \begin{cases}1&x_{3}=0\\ (0.3-x_{3})/0.3&0<x_{3}\leq 0.3\\ 0&0.3<x_{3}\end{cases}  \tag{13}\]  
\[\mu_{o}(x_{3})=