Intelligence Driven By The Fusion Of Digital Twin And Deep Learning Research And Realization Of Hui Oil And Gas Station System

With the introduction of intelligent manufacturing 2025,"Internet +" and other policies,the oil and gas industry has ushered in an intelligent technological revolution,the core of which is to promote the development of information technology in the oil and gas industry and realize the deep integration of artificial intelligence technology and the oil and gas industry.As the midstream industry of the oil and gas industry,oil and gas stations have the characteristics of complex operating systems,many types of management equipment,and high requirements for health management.It is of great scientific value and practical significance to explore how to effectively carry out information management of stations.Therefore,in view of the development status of digital oilfield construction in my country,the key technology of digital twin is applied to the operation and maintenance management of the station,combined with deep learning to detect abnormality in the sensor data generated during the operation of the station equipment,and the integration of digital twin and deep learning is designed and completed.Driven smart oil and gas station system.The overall research content of the thesis is divided into the following points:The 3D visualization technology and anomaly detection technology of the digital twin oil and gas station are studied,and a 3D virtual station is constructed which is mapped with the physical station.On this basis,a six-dimensional model framework of smart oil and gas stations is proposed,and a framework for abnormal detection and prediction of station equipment is proposed by combining deep learning and digital twins,so as to realize the safety management and operation and maintenance of smart oil and gas station equipment,and contribute to the development of smart oil and gas stations.The follow-up study of the station provides ideas.Aiming at the problem of anomaly detection of station equipment,an equipment health status diagnosis model based on MTAD-GAN network is proposed.Firstly,the network framework combining GAN and Long Short-Term Memory Recurrent Neural Network(LSTM-RNN)is used to capture the relationship of time series data and perform data reconstruction and discrimination.Then,spatiotemporal attention is introduced to learn the potential distribution characteristics of the data.Secondly,the anomaly scoring function is optimized through the scoring network.Finally,the anomaly scoring function G-score is used to judge the data,and the anomaly detection of the sensing time series data is realized.iming at the dependencies between sensors,an anomaly detection model based on graph neural network is proposed,which first uses graph embedding to capture the unique features of sensors,and learns the relationship between sensor data through graph structure,and then uses graph attention.The weights are used to explain the detected anomalies,predict the future behavior of the sensor data,and finally use the graph deviation score to explain the deviation of the structural relationship of the sensor graph,which improves the accuracy of the anomaly detection method.In order to realize the unmanned management of oil and gas stations,a smart oil and gas station simulation system was developed and built using Unity3 d based on the above key technologies.Combined with the actual needs of the station and the digital twin six-dimensional model framework,the virtual quadruped inspection system mapped with the physical model was imported.The robot realizes virtual-real interaction,and combines the deep learning anomaly detection technology to detect the health status of the station equipment,thereby realizing the remote monitoring and management of smart oil and gas stations driven by the fusion of digital twins and deep learning.