## 3 Results and Discussions
### Death Toll Prediction
Firstly, by analyzing the six correlation combinations between the four elements of the total number of accidents, the total number of deaths, the number of larger and above accidents, and the death toll of larger and above accidents, four of them are selected as having not reached the strong correlation, namely:  
Combination A: The relationship between the number of larger and above accidents and the total number of accidents;  
Combination B: The relationship between the death toll of larger and above accidents and the total number of accidents;  
Combination C: The relationship between the number of larger and above accidents and the total number of deaths;  
Combination D: The relationship between the death toll of larger and above accidents and the total number of deaths. Secondly, after correcting the relevant data, new data obtained is delayed, the new correlation is generated, and the forward and reverse processing is carried out. So that all indicator values are compressed between [0, 1]. The larger the value of the positive indicator, the better it is, and the larger the value of the negative indicator, the worse it is. The conversion formulas and metrics are described below:  
\[\text{Positive indicators}:\ \frac{x_{i}-minX}{maxX-minX}\ \text{Negative indicators}:\ \frac{maxX-x_{i}}{maxX-minX} \tag{8}\]  
M1: Initial correlation coefficient of the two elements in each combination;  
M2: The correlation coefficient of the two elements after the impact of the larger and above accidents lags by one month;  
M3: The correlation coefficient of the two elements after the impact of the larger and above accidents lags by two months;  
M4: Corrected correlation coefficient of the two elements in each combination;  
M5: Corrected correlation coefficient of the two elements after the impact of the larger and above accidents lags by one month;  
M6: Corrected correlation coefficient of the two elements after the impact of the larger and above accidents lags by two months. Among them, the M1 and M4 indicators are forward-oriented, and other indicators are reversed. Taking Combination A as an example, the results corresponding to each of its indicators are shown in Table 7.  
Thirdly, the above values are assigned to the CRITIC weights, and the situation with the largest proportion of indicators is selected. According to Table 8, it can be seen that after the impact of larger and above accidents is corrected and lags by one month, its impact on \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** \\ \cline{2-6}  & 0.883 & 1.000 & 0.833 & 0.706 & 1.000 & 0.522 \\ \cline{2-6}  & 1.000 & 0.707 & 1.000 & 1.000 & 0.480 & 0.851 \\ \cline{2-6}  & 0.874 & 0.624 & 0.970 & 0.768 & 0.340 & 1.000 \\ \cline{2-6}  & 0.499 & 0.080 & 0.590 & 0.945 & 0.214 & 0.282 \\ \cline{2-6}  & 0.353 & 0.533 & 0.630 & 0.255 & 0.711 & 0.595 \\ \cline{2-6}  & 0.442 & 0.114 & 0.375 & 0.243 & 0.000 & 0.241 \\ \cline{2-6}  & 0.829 & 0.519 & 0.000 & 0.922 & 0.034 & 0.403 \\ \cline{2-6}  & 0.638 & 0.142 & 0.249 & 0.728 & 0.191 & 0.000 \\ \cline{2-6}  & 0.190 & 0.000 & 0.258 & 0.000 & 0.844 & 0.398 \\ \cline{2-6}  & 0.000 & 0.381 & 0.242 & 0.566 & 0.345 & 0.167 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correlation coefficients for each indicator after forward and reverse. the number of deaths forecasts accounts for the largest proportion. Thus it is chosen to add 25% of the monthly changes in the number and death toll of larger and above accidents to 2020 to the remainder of the original trend decomposition. Finally, considering that the occurrence of larger and above accidents can not only have a warning effect on the construction field, strengthen the impact of education and management, but also lead to increase psychological pressure and aggravate the aging of equipment, etc. It will show two situations in terms of prediction: reducing and increasing the number of accident deaths. Predictions of the number of deaths will eventually result in a revised outcome interval.