As enterprises grow in scale and business becomes increasingly intricate, digitalization has emerged as a pivotal factor in enhancing financial operational capabilities and management efficiency. Nevertheless, the sheer volume of financial data and the interspersed presence of anomalies pose significant obstacles to this digital transformation. To address these challenges, we introduce a cloud computing-based digital financial management framework. This approach leverages serialization techniques to efficiently extract data characteristics, harnessing the compression and filtering capabilities of cloud computing to streamline data storage and access procedures. Experimental evidence demonstrates that this method effectively minimizes data errors while bolstering security, thereby serving as a robust foundation for the digitalization of enterprise financial management.
The effectiveness of e-commerce in empowering rural revitalization is often influenced by multiple factors, and there are complex nonlinear relationships between these factors, making it difficult to capture data characteristics and affecting the reliability of evaluation results. Therefore, in order to clarify the effectiveness of e-commerce in empowering rural revitalization and improve evaluation reliability, convolutional neural networks are introduced to conduct research on the evaluation method of e-commerce in empowering rural revitalization. Selecting evaluation indicators for the effectiveness of e-commerce in rural revitalization, and combining them with convolutional neural networks to construct an evaluation model. Based on its powerful nonlinear mapping ability, it automatically learns the nonlinear features in the data to more accurately evaluate the effectiveness of e-commerce in rural revitalization, and accurately obtain the coordinated development of rural e-commerce, farmers’ income increase, and rural revitalization in a certain city. The study’s findings reveal that this methodology not only aids in the transformation of agricultural production in Jiyuan City and the modernization of rural primary, secondary, and tertiary industries, but also catalyzes the transformation and enhancement of agricultural production and the integration of urban and rural development. Additionally, it contributes to narrowing the gaps between urban and rural areas in income, environment, and institutional frameworks, thereby promoting the achievement of rural revitalization in Jiyuan City. At the same time, it can also provide effective references for other regions of China in terms of working modes and methods to consolidate the victory of farmers in poverty alleviation and income generation and to realize the rapid revitalization of the countryside.
In order to improve the real-time control quality level of the linkage numerical control system, the research on the real-time control method of the linkage numerical control system based on human-machine hybrid enhanced intelligence was carried out. First, initialize and configure the linkage CNC system, and set the system to a stable and reliable working state; Secondly, import and analyze processing tasks to avoid processing accidents caused by code errors or parsing problems; On this basis, the real-time control program of the system is designed based on human-machine hybrid to enhance intelligence. The test results show that after the application of the proposed method, the synchronous action load of the system meets the requirements of the interpolation cycle, the response speed is fast, and the machining accuracy is significantly improved.
In order to solve the problem of information privacy leakage in storage and transportation logistics databases and improve the storage security of database information privacy, a chaotic mapping based encryption storage method for storage and transportation logistics database information privacy is proposed. Designed a distributed storage architecture for storage and transportation logistics database information, segmented and integrated the information of the storage and transportation logistics database, and encrypted the privacy of the database information using chaotic mapping; Implement secure storage of database information through access control. Through experimental verification, it has been proven that this method can reduce the workload of changing permissions, with an average encryption and decryption time of less than 10 ms. It can effectively improve the efficiency of encrypted storage and enhance the security of database information privacy storage.
In order to improve the quality of financial images, this paper proposes a financial image quality enhancement method based on deep learning and graph neural networks. Using nonlinear functions to compensate for lighting in financial images, converting them from RGB to SHV images with color tone, saturation, and brightness channels, achieving color space transformation in financial images. Utilizing deep learning techniques to repair brightness residuals in financial images, and utilizing graph neural networks for super-resolution processing to enhance image texture features, achieving quality enhancement of financial images based on deep learning and graph neural networks. The experimental results show that the application of the design method effectively improves the structural similarity and peak signal-to-noise ratio of financial images, making it suitable for enhancing the quality of financial images.
