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• W4282916034 abstract "In this era of data explosion, in order to make better use of data, people need to collect information to analyze and judge future decisions. For enterprises, the management of time, capital, and decision-making can directly affect the acquisition of enterprise profits. The sooner we analyze financial data, the earlier we use an intelligent system to assist management and promote operators to make reasonable business decisions. Traditional enterprise data management is very backward and has few functions. In order to achieve the purpose of optimal allocation of enterprise resources, information-based financial management. According to the financial status and functional requirements of enterprises, the specific functions of the system are designed. The method based on edge computing has a high reference value. This paper puts forward a financial management system of intelligent analysis. It can provide rich solutions to financial problems. Data mining and analysis of financial situation provide support for decision-making management of enterprises. The research results show that (1) after 10 times of inspection and adjustment, the system grade is all D grade, and there is no defect. (2) There is no abnormality in system response time; the average response time of F1, F2, F3, and F4 functions is in line with expectations. The fluctuation of time range is no more than 3 s; the overall average response time is 1 s. (3) After CPU detection, the CPU of the system is less than 70% of the expected utilization rate. The system is qualified. (4) The whale algorithm is used to combine new and old tasks. After comparison, it has an excellent performance in computing resources and delays energy consumption. Some follow-up improvement work needs to be optimized." @default.
• W4282916034 created "2022-06-16" @default.
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• W4282916034 date "2022-06-14" @default.
• W4282916034 modified "2023-09-30" @default.
• W4282916034 title "Intelligent Financial Data Analysis and Decision Management Based on Edge Computing" @default.
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• W4282916034 doi "https://doi.org/10.1155/2022/1133275" @default.
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