Industrial Time Series Big Data Sharing Method Based on Local Area Network Monitoring Log

Due to the involvement of a large number of real-time monitoring and data collection devices, the generated temporal data is huge and complex, posing a challenge for data sharing among multiple departments. To address this issue, this study proposes an industrial time series big data sharing method based on local area network monitoring logs. Deploy monitoring equipment in the local area network, collect real-time operation log data of industrial equipment, and perform missing value processing and missing data filling processing on the collected industrial time series data; Based on the data processing results, a blockchain and ROMA integrated platform based industrial time series data sharing model is constructed, and data sharing is achieved through the network layer, data layer, and application layer of the model. The experimental results indicate that this method can ensure both data consistency and integrity in data sharing, and has high application value.

I. Introduction

With the development of Internet, Internet of Things, edge computing and other technologies, a large number of time series data generated in the industrial field have been acquired and stored, which contain important information and value [1]. However, due to data dispersion, inconsistent formats, and issues such as data security and privacy, the sharing and utilization of industrial time series big data face many challenges. Industrial time series big data sharing can promote the integration and interoperability of industrial data, improve data utilization efficiency, strengthen collaboration and collaboration between upstream and downstream enterprises in the industrial chain, improve supply chain efficiency and flexibility, promote cooperation between academia and industry, and promote scientific research and technological innovation [2]-[3]. Therefore, studying the sharing methods of industrial time series big data is of great significance for solving the problem of industrial data silos and promoting the maximization of data value.