Accounting Management and Optimizing Production Based on Distributed Semantic Recognition

Abstract

Accounting management and production optimization are vital aspects of enterprise management, serving as indispensable core components in the modern business landscape. However, conventional methods reliant on manual input exhibit drawbacks such as low recognition accuracy and excessive memory consumption. To address these challenges, semantic recognition technology utilizing voice signals has emerged as a pivotal solution across various industries. Building upon this premise, this paper introduces a distributed semantic recognition-based algorithm for accounting management and production optimization. The proposed algorithm encompasses multiple modules, including a front-end feature extraction module, a channel transmission module, and a voice quality vector quantization module. Additionally, a semantic recognition module is introduced to process the voice signals and generate prediction results. By leveraging extensive accounting management and production data for learning and analysis, the algorithm automatically uncovers patterns and laws within the data, extracting valuable information. To validate the proposed algorithm, this study utilizes the dataset from the UCI machine learning repository and applies it for analysis and processing. The experimental findings demonstrate that the algorithm introduced in this paper outperforms alternative methods. Specifically, it achieves a notable 9.3% improvement in comprehensive recognition accuracy and reduces memory usage by 34.4%. These results highlight the algorithm’s efficacy in enhancing the understanding and analysis of customer needs, market trends, competitors, and other pertinent information within the realm of commercial applications for companies.