## 1 Introduction  
Wires and cables play a pivotal role as crucial conduits for both power and information transmission. In contemporary society, cables find widespread application across diverse industrial settings, including but not limited to chemical plants [1], industrial warehouses [2], nuclear power plants [3], wind farms [4], and many other industrial places [5]. Simultaneously, fires resulting from wire- and cable-related disasters exhibit the highest incidence rates compared to other categories of electrical products [6]. In the United States, an estimated 24% of industrial or manufacturing fires are attributable to electrical issues, predominantly stemming from cable failures [7]. Cable fires and their subsequent propagation can give rise to catastrophic disasters [6, 8]. Undoubtedly, cable fires represent a substantial menace to industrial production safety. Consequently, it is imperative to mitigate cable fire risks and guarantee the secure operation of cables. Risk analysis and assessment serve as the foundational pillars for the implementation of effective risk control and safety management. In the context of cable fire risk analysis, scholars have centered their focus on comprehending the underlying mechanisms responsible for cable fire incidents [9, 10, 11, 12, 13]. The combustion characteristics of wires and cables are intricately linked to the combustion attributes of their constituent materials and structural compositions, which are further influenced by operational conditions and environmental factors [11]. The cause factors of cable fire [10] include cable core overheating, external heating, arc fault, loose cables, wire insulation moisture absorption, cable aging, etc. The influencing factors and evolution mechanisms of cable fires studied so far have established an important foundation for cable fire risk analysis. However, applying these results to the prevention and control of cable fires in the working process has more practical significance. Fire risk assessment stands as a fundamental decision-making tool within the realm of risk control and safety management, providing potential risk information before the onset of a fire event. Presently, research endeavors in the field of cable fire risk assessment predominantly revolve around static analytical methodologies, including the weighted fuzzy Petri [14], the survey scoring method [15], and BN [16]. These methods integrate historical monitoring data and expert assessment data relative to risk factors to realize quantitative risk assessments based on multi-source information fusion [14; 17]. However, extant studies have regrettably overlooked several pivotal issues: (1) As the factors influencing cable safety undergo temporal variations, the evolution of cable fire risks inherently exhibits dynamic characteristics. Consequently, static risk assessment methodologies fall short of capturing the nuanced and evolving nature of cable fire risks, rendering them unsuitable for dynamic risk monitoring and the timely implementation of prevention and control measures. (2) Faced with the uncertainty in dynamic monitoring data [18; 19; 20] and the vagueness associated with expert experiential data [21], the development of a cable fire risk assessment model should focus on the stochastic and imprecise aspects of converting quantitative values and qualitative standards. Therefore, the establishment of a dynamic cable fire risk assessment methodology that takes into account the influence of uncertainty in the assessment process is indispensable. It is well-established that a dynamic Bayesian network (DBN) offers distinct advantages for dynamic risk analysis. It not only elucidates the intricate structure of risk transmission but also tracks the evolving probabilities associated with risk over time [22; 23]. Thus far, DBN has found widespread application in dynamic risk assessment across various domains [24; 25; 26]. Nevertheless, the utilization of DBN in the context of cable fire risk assessment remains uncharted territory.