# The Full Lifecycle Evolution Model of Accidents: A Case Study of Underground Metal Mines in China
## 4 Discussion
### Limitations and Future Research Directions  
The application scope of this model has been intentionally limited to the internal environment of production enterprises, without adequately considering the impact of external factors on organizational safety. Specifically, the roles of external influences, such as government regulation, societal oversight, and external safety resources, in enterprise safety management have not been explored in depth. Additionally, the societal losses resulting from production accidents--such as impacts on wealth, the environment, and intangible assets--are not reflected in this model. Government regulation plays a crucial role in enterprise safety management; strict regulatory policies and effective enforcement can motivate companies to enhance their safety practices. Societal oversight can also exert pressure on enterprises, prompting them to prioritize safety concerns. Furthermore, external safety resources, such as professional safety consulting agencies and technical service providers, can offer valuable expertise and technical support for safety management. However, the exclusion of these external factors from analysis limits the comprehensiveness of this model [15].  
Due to data constraints, this study's analysis of key safety management controls focuses solely on accident case data, utilizing manual analysis and identification methods for statistical purposes. This approach is restricted by the volume of data and lacks the timeliness required for effective safety management. In practice, organizations need to be promptly informed about safety conditions to implement effective preventive and control measures. Manual analysis and identification of accident case data is not only timeconsuming but also challenging when addressing large datasets. Moreover, relying solely on accident case data may overlook some potential safety hazards and risks. According to [PERSON]'s accident theory, there exists a proportional relationship between safety hazards, near-miss incidents, and production accidents. Therefore, including all relevant data within the enterprise in the analysis is crucial for enhancing the precision of controls and the effectiveness of measures. However, this creates a conflict between the vast amount of data and the efficiency of analysis.  
It is important to note that the collection of accident case data may be subject to certain limitations. Some accidents may be underreported or not reported at all, especially those with minor consequences or those that are considered normal operational variations. This could lead to a biased representation of the actual accident distribution and risk profile. Additionally, the accident analysis methods used in the collected accident reports may influence the data's reliability and scope. If the methodology focuses predominantly on specific cause-effect chains, other contributing factors might remain unexplored. Conversely, there might be instances where numerous accidents are attributed to human error, possibly due to the ease of identification or the prevalence of such factors in certain contexts.  
With the rapid advancement of big data analytics and large language model technologies, real-time monitoring and dynamic assessment of safety conditions within enterprises based on real-time hazard data have become feasible [48]. Big data analytics can process large volumes of data, extracting valuable insights to support decision-making in safety management. Meanwhile, large language model technology can quickly analyze and comprehend textual data, aiding organizations in better identifying safety hazards and risks. The accident analysis model proposed in this study provides a robust framework and foundation for risk assessment based on hazard sources. Future research will explore the applicability of this model in the context of intelligent accident prevention and dynamic real-time analysis of control measures.