# Cutting Path Planning Technology of Shearer Based on Virtual Reality
## 5 Simulation Experiment
### Analysis of Simulation Results
The matrix gives different weights to each index according to the different functions of each index in the overall evaluation. Under the condition that the safety level and other production costs are guaranteed, coal production pays more attention to the production efficiency directly linked to economic benefits \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Recovery Rate (SSE)** & **Production Efficiency (min)** & **Others (Safety Degree, Labor Cost, etc.)** \\ \hline Scheme1 & 13.89 & 8 & 8 \\ Scheme2 & 92.5 & 7 & 8 \\ Scheme3 & 125.73 & 5.5 & 8 \\ Scheme4 & 40.96 & 8.5 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation index. and the recovery rate that can maximize the use of resources. According to the general enterprise production experience [24], \(A=(0.4,\ 0.4,\ 0.2)\). * Determine the fuzzy evaluation matrix  
After determining the weights of the indicators, the evaluation index needs to be normalized and quantified one by one. A row in the fuzzy evaluation matrix R reflects the degree of membership of different schemes on this indicator. For the evaluation index with a specific value, the value of the optimal evaluation object can be set to 1 whereas, it is set according to the scoring level (\(0\leq\text{E}\leq 1\)) for other objects. For the index whose value is bigger and whose evaluation is better, the calculation method of evaluation value is: Evaluation value = specific value/optimal value. For the index whose value is smaller and whose evaluation is better, the calculation method of evaluation value is: Evaluation value = optimal value/specific value. When calculating the evaluation value of the recovery rate, a higher SSE indicates a lower recovery rate, and the evaluation value should be smaller. Among the four schemes, the one with the highest recovery rate is scheme 1, and the corresponding SSE value is 13.89. Then, the evaluation value of scheme 1 is set to 1, the evaluation value of scheme 2 is set to \(0.15=\frac{13.89}{92.5}\), the evaluation value of scheme 3 is set to \(0.11=\frac{13.89}{125.73}\), the evaluation value of scheme 4 is set to \(0.339=\frac{13.89}{40.96}\). Finally, the fuzzy evaluation matrix is obtained thus:  
\[R=\left[ \begin{array}{cccc}1&0.15&0.11&0.339\\ 0.7&0.808&1&0.646\\ 1&1&1&0.25\end{array} \right]. \tag{14}\]  
* Comprehensive evaluation  
Comprehensive fuzzy evaluation vector B is calculated by the combination of the fuzzy multiplication \(\odot\) and fuzzy evaluation matrix \(R;\ B=A\odot R\). The common fuzzy multiplications mainly include single-factor prominent type and weighted average type, in order to be able to objectively evaluate the impact of each index, the fuzzy multiplication chosen in this paper is the weighted average method [25]; \(M(\ \cdot,\ \text{v}),\ \ b_{j}=\sum\big{(}a_{i}\cdot r_{ij}\big{)}(j=1,2 \cdots m)\). Hence, the final evaluation was calculated as follows:  
\[B=A\odot R \tag{15}\]  
\[=\left[ \begin{array}{cccc}0.88&0.5832&0.644&0.444\end{array} \right]\]  
It can be observed that the four schemes above are sorted as schemes 1, 3, 4, and 2. The higher value of the test has a better comprehensive evaluation. When the minimum traction speed of the shearer is small, more accurate trajectory tracking can be achieved. With an increase in the minimum traction speed, the production efficiency of the shearer improves, whereas the accuracy of trajectory tracking decreases. On this basis, according to the requirements of different enterprises, the production process of different working parameters can be previewed and evaluated in a virtual environment.