基于多源数据融合的化工园区危险态势感知

化工园区存在危险品储罐、运输车等众多安全威胁,需要实时感知园区的危险态势,及时发现并排除潜在的安全威胁。传统方法是依靠危险品储罐实时监测等单一数据源进行危险识别,难以满足目前化工园区对安全状态评估的需求。从大数据分析的角度出发,整合化工园区内危险品储罐监测传感器、危险品运输车、地理信息等数据,基于危险品泄漏呈高斯扩散的特点,提出了一种多源异构数据融合的危险识别方法,实现园区的危险态势感知,并实时展示整个化工园区内的潜在危险区域。结合某化工园区的实际数据,验证了所提方法的有效性。

Danger situation awareness of chemical industry park based on multiple source data fusion

There are many safety threats in the chemical industry park, such as dangerous goods storage tanks and transport vehicles. The danger situation in the park need to be sensed in real time and potential safety threats must be discovered and eliminated in time. The traditional method relies on a single data source such as real-time monitoring of dangerous goods storage tanks for hazard identification, which is difficult to meet the current needs of the chemical park for safety status assessment. From the point of view of big data analysis, this paper integrates the data of dangerous goods storage tank sensors, dangerous goods transportation(DGT) and geographic information in the chemical park. Based on the characteristics of Gaussian diffusion of dangerous goods leakage, a multi-source heterogeneous data fusion method is proposed. The danger situation identification method realizes the dangerous situation awareness of the park and displays in real time the potential dangerous areas in the entire chemical park. Combined with the actual data of a chemical park, the effectiveness of the proposed method is verified.