Research on Economic Data Collection and Analysis Platform Based on Cloud Computing and Internet of Things Technology

The rise of the digital economy is shaping the future of business and society. In this digital era, data collection and analysis have become crucial to support decision-making, innovation, and efficiency improvement. Cloud computing and the Internet of Things technology, as the core driving forces of the digital economy, are opening new possibilities and providing strong support for data collection and analysis platforms. To broaden the scope of network big data applications, improve the accuracy of network data storage and management to a greater extent, and reduce the time for network data processing and control, a research method for network big data based on cloud computing and the Internet of Things in the context of the digital economy is proposed. The author first uses hierarchical network coding to transmit network data, based on the transmitted data, the CRC algorithm is used to calculate network data, and then the data is stored in a group storage manner, secondly, the high-precision query of network data is carried out using the hierarchical reverse stacking positioning method, thereby completing the research on network big data. Finally, to demonstrate the overall performance of network big data research methods based on cloud computing and the Internet of Things, a simulation experiment is conducted. The experimental results show that when the data query parameter epsilon is 8, the data query accuracy curve is unstable, and the query accuracy is low. When the data query parameter epsilon is 4-5, the data query accuracy curve is relatively flat, and the query accuracy is high. The method proposed by the author can comprehensively and concretely study network big data, improve data processing accuracy and network data calculation speed, increase network data storage capacity and query efficiency, reduce the loss rate of network data during operation, expand the operating range of network data, and provide a strong basis for subsequent research on network big data.