## 4 Conclusions
The correlation coefficient between the predicted value and the raw value is increased from 0.890 to 0.963, and the degree of fit is increased from 0.792 to 0.928. This method compensates for the fact that the relative error is affected by the base of the raw value itself, and the predictive value of the grey model in the accident prediction tends to be average and does not reflect the actual situation well, so the prediction result is closer to the real situation. This combined model avoids the defect that a single grey prediction is not suitable for target analysis and prediction with large fluctuations and verifies the validity of the prediction results. (3) By analyzing the impact of larger and above accidents in the country, it was found that when predicting the number of deaths, if the relationship between them and the number of accidents is not considered, the results will not reflect the development trend of safety accidents in the construction field in the real society. The method of highlighting the impact of larger and above accidents and quantifying them is adopted to distinguish the size of the warning effect of larger and above accidents in different periods on production and life, related personnel, and management. Since the occurrence of larger and above accidents has different effects on different regions of the country, the corrected residual values are added to the forecast results by taking positive and negative extreme values. The correlation coefficient between the predictive value of deaths and the raw value in 2019 is 0.994 and the degree of fit is 0.988, which ensures that the two are highly correlated and fitted, and obtains the predictive interval for the number of deaths in construction and production safety accidents in 2020. The relaxation of frequent accident control will cause the total number of accidents to show an upward trend, which is consistent with the predicted results. In the current construction safety management, on the basis of maintaining the management of more harmful accidents, the management of more frequent general accidents should be strengthened. In summary, the data preprocessing method used, and the combined prediction model constructed by this research, can well predict the situation of construction safety production accidents in China. Although since 2020, the impact of the COVID-19 epidemic on China's construction industry has been significant. Due to the analysis and processing of data as time series, and the wide range of applications of the combined model constructed, the prediction method proposed in this paper still has the possibility of being applicable in different countries and different fields. There are many studies on accident prediction, especially in the construction field, but they are often based on relatively stable accident trends, which is the limitation of this study. The impact of the COVID-19 epidemic is unprecedented, and in the short term, its impact and negative impact are large; In the long run, the epidemic will play a role in promoting the development of the construction industry with strong momentum. In the past three years, the epidemic has been affecting the development of all walks of life in the country, and its safety will have a direct impact on the construction field, and its impact on the economy will indirectly lead to changes in the security situation in the construction field. Therefore, in the future, the political economy and the world development situation can be further integrated into the research in the field of building safety, and various indicators can be integrated into a semi-open system to continuously supplement the indicators related to forecasting. In terms of prediction, a more ideal and universal model was constructed, and multi-field elements were incorporated to make breakthroughs in accident prevention and make research more practical. Further study is being conducted in this direction and will be reflected in our future work. Conceptualization, G.M. ; methodology, G.M. ; software, G.M. ; validation, G.M.