Accounting informatization is a crucial component of enterprise informatization, significantly impacting operational efficiency in accounting and finance. Advances in information technology have introduced automation techniques that accelerate the processing of accounting information cost-effectively. Integrating artificial intelligence, cloud computing, and edge computing is pivotal in streamlining and optimizing these processes. Traditionally, accounting informatization relied on system servers and local storage for data processing. However, the era of big data necessitates a shift to cloud computing frameworks for efficient data storage and processing. Despite the advantages of cloud storage, concerns arise regarding data security and the substantial data transactions between the cloud and source devices. To address these challenges, this research proposes a novel algorithm, Heterogeneous Distributed Deep Learning with Data Offloading (DDLO) algorithm. DDLO leverages the synergy between edge devices and cloud computing to enhance data processes. Edge computing enables rapid processing of large volumes of data at or near the data collection sites, optimizing day-to-day operations for enterprises. Furthermore, machine learning algorithms at edge devices enhance data processing efficiency, augmenting the computing environment's overall performance. The proposed DDLO algorithm fosters a hybrid machine learning approach for computing joint tasks and multi-tasking in accounting informatization. It enables dynamic resource allocation, allowing selected data or model updates to be offloaded to the cloud for complex tasks. The algorithm's performance is rigorously evaluated using key metrics, including computing time, offloading time, accuracy, and cost levels. By capitalizing on the strengths of edge computing, cloud computing, and artificial intelligence, the DDLO algorithm effectively addresses accounting informatization challenges. It empowers enterprises to process vast amounts of accounting data efficiently and securely while improving overall operational efficiency. Regarding time, using terasort in tasks offloading using DDLO consumes less milliseconds 0t 33 ms which is lesser than other techniques.

会计信息化是企业信息化的重要组成部分，显着影响会计财务的运营效率。信息技术的进步引入了自动化技术，可以经济高效地加速会计信息的处理。集成人工智能、云计算和边缘计算对于简化和优化这些流程至关重要。传统上，会计信息化依赖系统服务器和本地存储进行数据处理。然而，大数据时代需要转向云计算框架以实现高效的数据存储和处理。尽管云存储具有优势，但人们仍然担心数据安全以及云和源设备之间的大量数据交易。为了应对这些挑战，本研究提出了一种新颖的算法，即带有数据卸载的异构分布式深度学习（DDLO）算法。DDLO 利用边缘设备和云计算之间的协同作用来增强数据处理。边缘计算可以在数据收集站点或附近快速处理大量数据，从而优化企业的日常运营。此外，边缘设备的机器学习算法提高了数据处理效率，增强了计算环境的整体性能。所提出的 DDLO 算法培育了一种混合机器学习方法，用于计算会计信息化中的联合任务和多任务。它支持动态资源分配，允许将选定的数据或模型更新卸载到云端以执行复杂的任务。该算法的性能是使用关键指标进行严格评估的，包括计算时间、卸载时间、准确性和成本水平。DDLO算法利用边缘计算、云计算和人工智能的优势，有效解决会计信息化挑战。它使企业能够高效、安全地处理大量会计数据，同时提高整体运营效率。关于时间，在使用 DDLO 的任务卸载中使用 terasort 消耗的毫秒数更少（0t 33 ms），这比其他技术要少。