Research on the auxiliary decision method for safety risk management and control of electric power field operations based on multimodal causal knowledge graph

Risk management and control of on-site power operations is an important link in the power production process. Traditional methods that rely on manual execution of safety rules lack proactive prevention and control capabilities, and insufficient efficiency leads to an increasing frequency of safety issues in power production. Therefore, it is urgent to adopt intelligent methods to improve the level of safety risk management and control. In response to this issue, the article proposes a multimodal causal knowledge graph based auxiliary decision-making method for safety risk management in power field operations. Based on the characteristics and requirements of safety risk management for on-site power operations, first obtain multimodal data related to safety risk management for on-site power operations, including video, photo, and text information collected by end devices, as well as historical archive data in various system databases. Then, based on historical risk management rules, the causal relationships of the core elements of on-site work safety issues are sorted out, and the data involved in the multiple types of antecedent influencing factors of historical faults and safety accidents are taken as the causes, and the faults and safety issues are taken as the results, forming a complete causal knowledge sample and constructing a causal effect estimation learning model. This model can output the safety risks of on-site power operations, forming a causal knowledge graph to provide auxiliary support for early warning and pre control decision-making of safety risks in on-site power operations.