Fusion Analysis and Digital Realization of Inspection and Repair of High-Speed Railway Engineering Equipment

High-speed railway engineering equipment is the basis of railway transportation, and transportation safety is closely related to the equipment operating state. In order to help inspectors and managers of the engineering departments to conduct inspection and repair more efficiently, solve problems such as the lack of intuitiveness of inspection status, unreasonable setting of inspection cycle days, and frequent occurrence of equipment accidents caused by seasonal weather changes, this paper uses deep learning to establish an automatic disease recognition model based on Convolutional Neural Network. Through the data collected from several high-speed railway workshops for verification, it is concluded that the model can realize the automatic recognition of disease types, the training accuracy reaches 97%, and the verification accuracy reaches 76%. Meanwhile, based on big data technology, this paper combines Convolutional Neural Network and Long Short-Term Neural Network, establishes the equipment status judgment model, and builds the inspection cycle algorithm based on equipment status. Through the data collected from multiple engineering departments, the equipment state judgment model can capture the key information from the inspection records and can thus accurately judge the equipment operating status. The accuracy of the predicted inspection times reaches 85.7%. Finally, through the digital implementation case, it is fully proved that the design and fusion of these two applications can provide sufficient technical support for the inspection and repair of high-speed railway engineering equipment and can thus provide a comprehensive and reliable data basis for real-time and efficient inspection and repair decisions.