Voice Assistance and Big Data Financial Management Based on High-Resolution Imaging Algorithm

Abstract

With the combination of information technology and economic fields, the amount of data has been greatly increased, and big data has begun to be valued by modern enterprises. As a new IT technology, it has had a huge impact on enterprise management, financial and management models, and business processes. Big data will surely become the basis of enterprise competition and management, and the use of information will have a decisive impact on the operating efficiency of enterprises. Big data sets put forward new requirements for corporate financial management. This article is the research goal of voice assistance and big data financial management based on high-resolution imaging algorithms. This paper establishes the specific process of the speech recognition model and high-resolution imaging algorithm based on the genetic algorithm of big data, and compares the experimental data of this paper with the data obtained from the reference literature and the Internet. Big data puts forward new requirements for financial management. It integrates high-resolution imaging algorithms and voice assistance into financial management based on big data, and studies the academic value and practical application value of financial management based on big data. Combined with actual data practice, it proves the feasibility and practicability of the research direction of this article. According to the experimental research in this article, the voice assistance and big data financial management based on the high-resolution imaging algorithm proposed in this article, adding voice assistance to the financial management can make the financial management run better, and the customers can obtain better data. The changes to the management staff can get management errors in a more timely manner, so that they can be modified in a more timely manner. In the use of genetic algorithms based on big data to optimize speech acquisition and recognition, experimental data shows that the highest recognition rate of optimized speech assistance is 98% close to 100%.