Accounting professionals are increasingly being encouraged to shift their focus from conventional accounting to accounting information as a result of new management strategies and ideas. Cybercrime and other attempts to exploit weaknesses in online systems have become more common in recent years. By introducing the concept of cloud computing and analyzing its logical structure, this research applies the technology and design model to the development of an Accounting Information Management System (AIMS). In accounting information technology administration, efficient resource allocation and decision-making are crucial for optimizing financial performance and strategic planning. Algorithms for dynamic planning are a useful tool in meeting these issues. To maximize efficiency in an accounting group’s allocation of resources, this study employs a dynamic planning method called value iteration. The research presented a new Bayesian optimized Restricted Boltzmann machine (BO-RBM) for acquittal IT management. The data set was first gathered and then pre-processed using z-score normalization. Then, an improved genetic algorithm was used to feature selection. After the system’s design and construction are complete, BO-RBM utilizes to both specify the cloud platform’s distributed storage mode and assess the cluster’s performance. The results show that the algorithm may boost financial performance, increase cost management, and accomplish strategic goals in the IT administration of accounting. The research in this study demonstrates that the cloud platform for handling massive amounts of data may accelerate processes and complete tasks quickly.

中文翻译：

---

动态规划算法在会计信息技术管理中的实现

由于新的管理策略和理念的出现，越来越多的会计专业人士被鼓励将他们的注意力从传统会计转向会计信息。近年来，网络犯罪和其他利用在线系统弱点的尝试变得更加普遍。本研究通过引入云计算的概念并分析其逻辑结构，将其技术和设计模型应用于会计信息管理系统（AIMS）的开发中。在会计信息技术管理中，有效的资源配置和决策对于优化财务绩效和战略规划至关重要。动态规划算法是解决这些问题的有用工具。为了最大限度地提高会计集团资源配置的效率，本研究采用了一种称为价值迭代的动态规划方法。该研究提出了一种新的贝叶斯优化受限玻尔兹曼机 (BO-RBM)，用于无罪 IT 管理。首先收集数据集，然后使用 z 分数标准化进行预处理。然后，采用改进的遗传算法进行特征选择。系统设计和构建完成后，BO-RBM既可以指定云平台的分布式存储模式，也可以评估集群的性能。结果表明，该算法可以提高财务绩效、加强成本管理并实现会计 IT 管理的战略目标。本研究表明，处理海量数据的云平台可以加速流程并快速完成任务。