Oil and gas storage and transportation pipeline is one of the most critical infrastructure, the transportation medium generally has flammable and explosive hazard characteristics, once the production safety accident occurs, the social impact will be extremely harsh. The failure situation of oil and gas storage and transportation pipeline is systematically studied, the event tree model is constructed, and the calculation method of leakage of different forms of media is proposed. Using AI and other intelligent means and the basic algorithm model of oil leakage monitoring, an intelligent monitoring and early warning system covering the common pipe corridor area is established to monitor the alarm events such as running and dripping leakage at the pipeline sealing point and the surrounding conditions. Through data transformation, visual algorithm is used to identify the objects, environments and their interrelated relationships under the working scene, build a knowledge map, judge the matching of the current state and the normal state, and realize the early warning functions such as leakage. The research results effectively improve the efficiency of emergency response and safety risk prevention and control ability of oil and gas storage and transportation pipelines.
In order to cope with all kinds of emergencies on the freeway and improve the emergency response capability of the freeway system, the emergency response capability of the freeway road network is quantitatively assessed. Based on the system resilience theory, a freeway emergency response capability assessment index system is constructed from three aspects: resistance capability, adaptive capability and recovery and optimization capability. The concept of cloud model is introduced, and the traditional hierarchical analysis scalar theory is modified to show the randomness, fuzziness and dispersion of the evaluation system with three numerical characteristics of expectation, entropy and super entropy, so as to determine the relative weight of each index and the cloud numerical characteristics of risk value; finally, the cloud model is used to quantify the randomness and fuzziness of each index, generate the cloud map of each risk evaluation level, and obtain the overall evaluation results. The results show that the overall emergency response capability score is 91.646, which is in the good level and should focus on strengthening the road network recovery and optimization capability and adaptive capability construction to improve the comprehensive resilience of the road network. This method can be used to quantitatively assess the emergency response capability of the freeway network and provide a scientific basis for the improvement of the emergency management capability of the road network system.
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