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.
The amount of data in online practical training is relatively large, and there are certain risks in practical teaching, which makes the design of online practical training platforms difficult. However, feedforward neural networks can process and analyze a large amount of data, provide personalized learning paths and feedback for students, and conduct practical training in a virtual environment. Students can try various operations without any risks. Therefore, design a network training and teaching platform based on feedforward neural networks and virtual simulation. The hardware of the network training and teaching platform is designed using the B/S architecture, which simplifies development and maintenance through centralized server processing and unified browser access. At the same time, a hardware system consisting of a main control center platform, intelligent network devices, etc. is constructed by combining feedforward neural networks and virtual simulation technology. A realistic network training scene and virtual character model were constructed through virtual simulation technology, and fine modeling and texture processing were carried out using 3ds Max and Photoshop to achieve an immersive learning experience and improve learning interest. A feedforward neural network model was designed to enhance students’ practical abilities and innovative thinking. Through virtual training scenarios and customized teaching content, precise grasp of student learning characteristics and efficient provision of teaching resources were achieved. The experimental results show that all functions of the platform can operate normally and meet expectations. In terms of performance, it has high throughput and can meet the processing needs of most user requests.
Conventional THz spectral imaging enhancement methods mainly use Local Linear Embedding (LLE) local linear embedding algorithm to complete complex high latitude projection, which is vulnerable to the influence of local distance mapping relationship of samples, resulting in high Root Mean Square Error (RMSE) root mean square error. Therefore, a new Terahertz (THz) spectral imaging enhancement method needs to be designed based on Graph Neural Network (GNN). That is, the terahertz spectral imaging image is preprocessed, the terahertz spectral imaging enhancement model is constructed using graph neural network, and the terahertz spectral imaging enhancement algorithm is designed to achieve terahertz spectral imaging enhancement. The experimental results show that the designed THz spectral imaging enhancement method based on graph neural network has low RMSE root mean square error of different images after enhancement, which proves that the designed THz spectral imaging enhancement method has good enhancement effect, reliability, and certain application value, and has made certain contributions to meet the complex and changeable spectral imaging application scenarios.
In order to achieve the goal of accurate recommendation of oral English online learning resources and optimize the learning experience, a brand-new accurate recommendation method of learning resources is proposed by using multi-source information fusion. Firstly, based on multi-source information fusion, a personalized model of learners is established to master learners’ interests, knowledge level and intentions; Secondly, the online learning resource database of spoken English is constructed, and the resources suitable for learners are initially found through screening and sorting functions; On this basis, an accurate recommendation algorithm for learning resources is designed, and the resource recommendation result with the highest matching degree is generated according to the similarity between the tag information of learning resources and the user's interest characteristics. The test results show that this method is effective in accuracy, recall, F1 scores show significant advantages, which can more accurately and comprehensively cover the learning resources that users may be interested in, and provide learning resources that meet their expectations.
Standardized individualized recommendation techniques for ideological and political content predominantly use the community engagement score (CES) algorithm to judge the user's characteristic behavior, which is vulnerable to changes in the weight of the recommendation momentum factor, resulting in poor recommendation performance. Therefore, a new personalized recommendation method for ideological and political resources needs to be designed based on reinforcement learning and evolutionary computing. In essence, a learner model has been formulated for the personalized recommendation of ideological and political resources. By harnessing reinforcement learning and evolutionary computing techniques, an algorithm tailored for personalized recommendations of these resources has been devised. Consequently, a personalized recommendation center for ideological and political resources has emerged, facilitating tailored suggestions to individuals. Experimental outcomes reveal that the proposed personalized recommendation approach, which incorporates reinforcement learning and evolutionary computing, exhibits a notable recommendation hit rate, average reciprocal ranking, and normalized cumulative gain, which proves that the designed personalized recommendation method for ideological and political resources has good recommendation effect, reliability, and certain application value.
Financial risk identification methods are often based on financial statement analysis and financial index calculation, and it is difficult to guarantee the accuracy of identification results when dealing with a large number of complex relationships, therefore, a financial risk identification method based on deep learning and neural network is proposed. Multiple risk indicators are selected, indicator data is collected and pre-processed, deep learning is combined with neural network to build network model, and the model is trained by sample data to realize financial risk identification. The experimental results show that the accuracy of the designed method is as high as 98.24
In blended English teaching, data can come from multiple sources, such as online learning platforms, classroom interaction systems, student assignments, test scores, and so on. There are structural differences in these data, which lead to too high data fusion polarity, so a data fusion method for blended English teaching based on Kalman filtering is proposed. The hybrid English teaching data features are extracted, and the data fusion is completed by using Kalman filtering to generate the hybrid English teaching data fusion process. The experimental results show that the hybrid English teaching data fusion extreme deviation of the proposed method is low, and it has a better hybrid English teaching data fusion effect.
Due to the influence of different sources and types of multimodal teaching data in the economic law course, the data fusion effect is not good. Therefore, a multimodal teaching data fusion method is designed for “economic law” course of finance and commerce majors. The structural characteristics of multimodal teaching data of “economic law” course are extracted, and the teaching data is thoroughly analyzed through various modalities, encompassing text, image, audio, and video, to extract its intricate inner structure and vital information. The multimodal teaching data fusion model integrates various forms of information such as text, images, audio, and video to provide students with a richer and more three-dimensional learning environment. This diversified way of presenting information can stimulate students’ interest in learning, improve their participation and learning effectiveness. The construction of a multimodal teaching data fusion model for the “economic law” course, specifically tailored for finance and commerce majors, has been successfully accomplished, and the teaching data structure and information are effectively integrated and transformed into the information that meets the multimodal teaching mode of “economic law” course and meets the teaching needs. The multimodal teaching data fusion function is optimized to enhance the complementarity and synergy between the data, reduce the information redundancy and conflict between different modal data, so as to adapt to different teaching scenarios. The experimental results show that the multimodal teaching data fusion of this method is effective and can be applied in real life.
Aiming at the problem that the fault prediction accuracy of distribution network transformer is low and cannot provide reliable basis for transformer operation and maintenance, digital twin and deep learning are introduced. By constructing the digital twin model of distribution network transformer, classifying the movement state of distribution network transformer; Innovatively deep learning fault samples to achieve intelligent prediction of transformer operation status in distribution networks. The experimental results show that the prediction results of this method are more accurate and the calculation time is shorter. This method has guiding significance for the knowledge system of digital twins in the application of distribution transformer fault prediction.
In order to improve the operational accuracy of the remote monitoring system for the health of elderly people living alone, ensure accurate remote monitoring and timely warning of their health status, this study adopted physiological parameter feature fusion technology and developed a new monitoring system. The hardware part of the system includes a main control module, a data acquisition module, a display module, a communication module, and a video surveillance module. The software part is implemented through main program design, physiological parameter collection, physiological signal feature extraction, remote health monitoring of elderly people living alone based on physiological signal feature fusion, anomaly detection and warning, and other links. After testing, the remote monitoring data accuracy of the system has significantly improved, with an accuracy rate of over 98
Virtual simulation experimental teaching usually involves a large amount of data, which may come from different sensors, measurement devices and simulation software.In order to effectively manage and integrate these data, a multi feature fusion method is proposed to construct a virtual simulation teaching data extraction model, ensuring completeness and accuracy, and optimizing the extraction of correlations. Experimental verification, multi feature fusion data extraction method, high consistency between reality and chance.which proves that the designed teaching data extraction method has a better extraction effect, is reliable, it has a great promoting effect on improving the teaching quality of virtual simulation experiments.
This paper offers an extensive review of four pioneering models in the realm of 3D computer vision and generative modeling: GET3D, SDFusion, ATT3D, and GaussianDreamer. Each model is distinguished by its innovative approach to tasks such as depth estimation, 3D reconstruction, point cloud processing, and 3D asset generation. GET3D excels in generating high-quality 3D meshes from 2D images, while SDFusion specializes in 3D shape completion and reconstruction using a latent diffusion model. ATT3D stands out for its attention mechanism that enhances point cloud analysis, and GaussianDreamer uniquely merges 2D and 3D diffusion models for rapid text-to-3D asset creation. Despite their advancements, these models grapple with challenges including computational efficiency, handling complex scenes, and ensuring the realism of generated content. This review delves into their operational principles, the challenges they face, and potential opportunities for future research, aiming to spur further innovation in the field of 3D computer vision and generation.
Due to the fact that traditional information fusion methods mostly rely on pre established theoretical mechanism models, the complexity of multi-source information in practice leads to certain deviations in the theoretical mechanism models, which affects the accuracy of information fusion. To address this issue, this study proposes a data-driven strategy based on the fusion of multi-source personalized news page information. Firstly, a high concurrency distributed crawler framework is used to obtain multi-source news pages online, and multi-source information is extracted from the obtained news pages. Then, based on the data-driven strategy, the multi-source information is fused to realize the personalized recommendation of multi-source news webpage information. The experimental results show that after applying this method, the root mean square error of the fusion result of the personalized news webpage information is only 0.17, which indicates that the method achieves more accurate fusion results.
Conventional multiple backup methods often rely on fixed backup strategies and cycles, and lack real-time awareness and dynamic adjustment capabilities for data changes, resulting in high space occupancy. In order to solve this problem, a multiple backup method of enterprise financial data based on active learning algorithm is designed. Based on active learning algorithm, it can effectively calculate the similarity of enterprise financial data, so as to accurately identify redundant or duplicate data. Through deduplication of these data, it ensures that the data in the backup process is clear and accurate, avoids the storage of invalid data, and realizes multiple backup of enterprise financial data. The comparison experiment shows that when the data volume is 500MB, the space occupancy rate of method 1 is 95
In order to improve the quality of clustering of digital English teaching resources, this paper introduces deep learning and conducts a research on the clustering method of digital English teaching resources. This method designs a cluster quality evaluation index of digital English teaching resources from multiple dimensions to evaluate the level of digital English resources. Collect English resources and establish a digital English resource bank. On this basis, we use deep learning to design resource clustering model. Finally, it realizes the accurate calculation of resource distribution density and designs the clustering process of digital English teaching resources. The findings from the experiment reveal that this technique has the capability to reach the maximum F-value measurement while maintaining a rapid pace, thereby leading to a substantial enhancement in the clustering quality of digital English instructional materials.
Volleyball training apparel, a crucial aspect of sports equipment, must not only cater to the functional requirements of athletes but also exhibit an alluring and attention-grabbing aesthetic design, and proposes a method of generative adversarial network-based automatic generation of decorative patterns for volleyball training apparel. Design the generative adversarial network model structure, train the model, construct the clothing pattern feature data set, provide sample material for clothing pattern feature extraction, preprocess the clothing pattern based on generative adversarial network, and finally realize the reconstruction of the volleyball training clothing pattern. The experimental outcomes demonstrate that, in comparison to conventional pattern design methodologies, this approach enables the realization of a stylized design for the pattern, offering a unique and creative alternative, and not only ensure the completeness of pattern elements, but also ensure the integrity of pattern elements, and the quality of pattern design.
