## 3 Results and Discussions
### Death Toll Prediction  
According to the above research, it was found that the trend decomposition of the time series was first analyzed and then predicted, and the prediction accuracy of the results was higher than that of the direct forecast. So, only the prediction after the trend decomposition was explored in the prediction of the number of deaths. Setting the ARIMA (4, 2, 11) and ARIMA (4, 0, 4) processes, the monthly forecast of construction deaths in 2019 and 2020 is shown in Table 6. The correlation coefficient between the true number of monthly accident deaths and the predicted value was 0.906, the degree of fit was 0.821, and the relative error was 0.33%, indicating that in terms of reflecting the real situation, the prediction of the number of deaths was not as good as the prediction of the number of accidents. Although the number of deaths is predicted as a single time series, the relative error between the predicted value and the true value in 2019 was extremely small. Through the analysis of the death number data itself, it can be seen that the number of deaths in the accident prediction is different from the number of accidents which can have an intuitive impact, and it is impossible to make accurate predictions based on the pure time series. Therefore, this paper further adjusted the prediction results by considering the impact of larger and above accident factors. For example, at the level of death data, an accident in which 9 people died and 3 accidents in which 3 people died are equal in quantity, but in real life, the accident of 9 deaths is far greater than that of 3 deaths, and the warning role played in the later construction process will be different [30]. Therefore, the rescale of larger and above accidents in 2010-2019 with detailed records of monthly accidents is intended to highlight its impact on the development trend of construction and production safety accidents. The specific scales are as follows:  
\[ \begin{array}{l}X=\sum_{i=0}^{n}m_{i}-2\ (i=0,\ 1,\ 2,\ \ldots,\ n)\\ Y=\sum_{i=0}^{n}m_{i}\times\alpha\ (i=0,\ 1,\ 2,\ \ldots,\ n)\ \alpha=m_{i}-2 \end{array}  \tag{7}\]  
\(X\): The impact of the number of larger and above accidents each month per year. \(Y\): The impact of the number of deaths in larger and above accidents per month per year. \(m_{i}\): The number of deaths per month of each larger or above accident. The CRITIC weighting method [31] is used for rescaled data, which is objective empowerment to quantify the volatility and relevance of the data. If the standard deviation of the data is larger, the greater the fluctuation, and a high weight value is assigned. If the correlation values between the indicators are smaller, the greater the conflict, and a high weight value is assigned [32]. Finally, the two indicators are multiplied to obtain the amount of information, and the final weight is obtained after normalization. The specific steps are as follows: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Trend** & **Seasonal** & **Residual** & **Predictive Value** & **Raw Value** & **Relative Error** \\ \hline
2019 & 914 & 0 & \(-\)13 & 901 & 904 & 0.33\% \\ \hline
2020 & 1007 & 0 & 23 & 1030 & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Monthly forecast and additional results of housing municipal engineering production safety accidents deaths after trend decomposition.