## 5 Conclusions  
Considering the dynamic evolution characteristics of cable fire risk and the uncertainty of the evaluation data, this study proposes a hybrid CM-enabled DBN method for cable fire risk assessment. First, a dynamic fire risk assessment framework was developed, where three modules were proposed: (i) cable fire DBN establishment, (ii) CM-based data processing construction, and (iii) dynamic fire risk reasoning and analysis. Subsequently, a DBN was applied to determine the dynamic risk probability of cable fires, and a CM was applied to deal with the uncertainty of the evaluation data. Subsequently, the BM and FV importance measures were introduced to provide a reference for dynamic risk prevention and control. Furthermore, a case study of the cable of a low-voltage distribution room at the Chongqing Tobacco Logistics Center was implemented to validate the proposed method. Dynamic risk profiles and importance changes were obtained, and the risk reasoning results were also discussed, which illustrates the feasibility and rationality of the proposed  
Figure 16: Comparison of the fire risk probability obtained using different methods in monitoring time.  
method. Finally, the assessment results of the proposed method and the traditional DBN method were compared, which confirmed that the results based on the proposed hybrid CM-enabled DBN method are more reliable. According to the results and discussion, the primary contributions of the proposed method are as follows:  
1. For the first time, DBN was applied to the dynamic risk assessment of cable fires, which not only realized the dynamic reasoning of fire risk values but also clearly depicted the state changes in risk factors.
2. CM was used to convert sensor monitoring data and expert scoring data into the state membership degrees of the root node; then, the membership degrees were input into the DBN as a prior probability. Using CM-based data processing, randomness and fuzziness are emphasized in the risk assessment process, and additional information can be mined from the data.
3. BM and FV importance was introduced to identify the key nodes from the two dimensions of risk prevention and risk control, respectively. Dynamic importance analysis can be realized based on its associated reasoning, which is conducive to reasonable risk prevention and timely risk control.  
Given the above explanations, this study enables a dynamic risk assessment for cable fires by considering the uncertainty of the evaluation data. Based on the proposed framework, reasonable structure learning and parameter learning methods can be introduced in the future to develop a more scientific DBN, and investigating the importance of risk prevention and control in multi-node joints will be the focus of future research. Moreover, the proposed method can provide support for the real-time risk assessment of cable fire risk, and the assessment is expected to achieve wider application value in industrial fire prevention and risk management by integrating it with Internet of Things (IoT) technology.  
Conceptualization, Z.X.; Methodology, Z.X.; Software, Z.X.; Formal analysis, Y.Y.; Investigation, G.H. and X.G.; Writing--original draft, S.G.; Writing--review & editing, X.G.; Supervision, G.H., Y.Y. and X.G.; Project administration, G.H.; Funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) (No. FRF-IDRY-21-016) and the Central Basic Research Fund project (No. 282022Y-9462).  
The data used in this study has been provided in this article.  
The authors declare no conflict of interest.