Construction of Accounting Internal Control Management Platform Based on IoT Cloud Computing - Song, Li (Wireless communications and mobile computing, 2022)
The Internet of Things is a network that interconnects all items on the Internet through radiofrequency identification, infrared sensors, global positioning systems, laser scanners, and other sensing equipment according to the agreed communication protocol to achieve intelligent identification, positioning, analysis, monitoring, and management. Internal accounting control is an accounting measure established for the enterprise to identify and analyze the operating conditions and to be responsible for the enterprise assets. Accounting internal control is the core of internal control, and the rational use of internal control theory is conducive to promoting the healthy development of enterprises. According to the control object, it can be divided into financial risk control, operational risk control, human resource risk control, strategic risk control, and information distortion control. This paper is aimed at studying the construction of an accounting internal control management platform based on the Internet of Things cloud computing. How the internal accounting control system adapts to the development of the socialist market economy is the current research focus. In view of the current development status of the control system, this paper combines the internal control with the accounting information system and explores the enterprise management from the strategic management level and the operational management level. This paper mainly builds an accounting big data analysis platform based on cloud computing and believes that the platform should have the function of financial comprehensive analysis. The experiment in this paper found that the actual management cost of the company in 2017 was 2630 million yuan, and the predicted management cost was 2600 million yuan. The gap between the predicted data and the actual data is getting closer and closer, indicating that the prediction platform is effective.