| Popis: |
The rapid growth of the urban rail transit industry has resulted in a surge in the volume of operational data generated by urban rail trains. However, traditional full-data storage methods have become inadequate in handling the escalating demands for data processing and storage. To address this, this paper presents an innovative optimization strategy that utilizes the data hopping technology for data processing and storage. This strategy aims to significantly reduce processing demands for urban rail train operational data, minimizing the occupation of storage resources, by comparing data from adjacent time sequences to accurately identify and store characteristics of data changes. In subsequent site experiments, the proposed approach achieved a 97.5% reduction in data processing demands and a 97% saving in storage space, while ensuring data security and stability. The study outcomes not only support the endeavor to effectively ease the burden on data transmission and storage, but also provide a robust data foundation for the intelligent operation and maintenance of urban rail transit. |
