Consistent Hash-based Data Storage Method for Electronic Archiving of Financial Reports
Due to the existence of massive financial report electronic archive data on multi-temporal and spatial scales in the enterprise financial management system, the financial report electronic archive data grow exponentially with the advancement of the enterprise operation time. At this time, the use of distributed storage system for data storage may exhibit load balancing imbalance phenomenon. In order to realize the effective storage of financial report electronic archive data, a data storage method based on consistent hash is proposed. Through the use of threshold-based volatility coefficient processing method for reconstructing and de-noising high-frequency signals of financial data; after noise reduction of the corresponding data timestamp as the core attribute of data storage, and according to the data timestamp of the data is equivalent to line order division; based on the consistency of hash algorithm, the construction of the load balancing adjustment mechanism helps to achieve the effective storage of the financial report of the electronic archiving data. Experimental results show that: using the design method to store the data delay time is relatively short, the maximum value of the storage delay time is 0.033 ms, the minimum value is 0.008 ms. The results indicate that the proposed design method for the electronic archiving of financial reports offers a faster data storage speed and a stronger overall data processing capacity. It can effectively achieve real-time storage of distributed data, demonstrating high feasibility and reliability in the data storage aspect of financial report electronic archiving. It has high feasibility and reliability in the electronic archiving data storage of financial reports.
Authors
- Ming Zhang
Institutions
- Chengdu University of Technology (CN)
Publication Details
- Journal
- International Journal of High Speed Electronics and Systems
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1142/s0129156425404899
- Citations
- 1
- Primary Topic
- Big Data Technologies and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.17