At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.

目前，企业已经引入了物联网（IoT）技术来监视和评估油库的安全状态，从而可以从工厂收集大量的多源监视数据。但是，传感器监控数据通常不准确且模糊。为了提高基于多源传感器数据的风险预防和控制的可靠性，本研究提出了一种基于云模型（CM），Belief Jensen-Shannon（BJS）散度和Dempster-Shafer（CM）的CM-BJS-DS模型。 DS）证据理论。首先，确定了事故的相关评估因子及其不同风险等级的阈值间隔，并构建了与不同风险等级相对应的模糊云隶属度函数。然后，，在识别的风险评估框架下生成了基本概率分配（BPA）。最后，通过改进的证据融合模型对BPA进行预处理，并评估事故风险等级。根据监视数据，进行了案例研究，以评估由于液化石油气（LPG）储罐泄漏而引起的蒸气云爆炸（VCE）事故的风险等级。结果表明，该方法具有以下特点：（i）基于监测数据构造了双酚A，降低了施工过程的主观性。（ii）与单传感器相比，多传感器融合评估产生了更具体的结果；（iii）当处理高度矛盾的证据时，该方法的评估结果显示出较高的置信度。