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<p>This study aims to analyze the intelligent integration of financial technology innovations in small and medium enterprises (SMEs) based on sustainable computing. The research focuses on designing a novel management pipeline and addressing energy consumption issues across multiple levels of the system. We developed a novel management pipeline where each application’s ApplicationMaster is responsible for core tasks such as resource scheduling and coordination. The applications include traditional MapReduce tasks and directed acyclic graph (DAG) tasks. In the wireless ad hoc network, end-to-end delays are analyzed by considering transmission delays, propagation delays, channel random access processes, link rerouting when breaks occur, and link switching. An intelligent model is introduced to optimize these processes. The study also explores energy consumption issues across hardware, operating systems, virtual machines, and data centers, dividing storage system energy optimization into hardware-based and scheduling-based approaches. The experimental results validate the effectiveness of the proposed model in resource scheduling, financial risk analysis, and energy optimization. The intelligent model demonstrated significant improvements in managing end-to-end delays in wireless ad-hoc networks. In addition, the study showed that the proposed energy optimization methods effectively reduce energy consumption in storage systems by addressing both hardware and scheduling aspects. This research provides SMEs with a new intelligent management approach for financial technological innovation. It also offers practical solutions for energy optimization, contributing to sustainable computing practices. The findings can assist enterprises in improving efficiency and reducing operational costs through better resource management and energy use. The study’s applicability and effectiveness need further validation in larger-scale real-world environments due to experimental constraints. Future research should focus on extending the model’s application to diverse operational settings and conducting long-term performance assessments.</p>

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Intelligent combination of SMEs’ technological financial innovation based on sustainable computing

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Pingping Qiao

摘要

This study aims to analyze the intelligent integration of financial technology innovations in small and medium enterprises (SMEs) based on sustainable computing. The research focuses on designing a novel management pipeline and addressing energy consumption issues across multiple levels of the system. We developed a novel management pipeline where each application’s ApplicationMaster is responsible for core tasks such as resource scheduling and coordination. The applications include traditional MapReduce tasks and directed acyclic graph (DAG) tasks. In the wireless ad hoc network, end-to-end delays are analyzed by considering transmission delays, propagation delays, channel random access processes, link rerouting when breaks occur, and link switching. An intelligent model is introduced to optimize these processes. The study also explores energy consumption issues across hardware, operating systems, virtual machines, and data centers, dividing storage system energy optimization into hardware-based and scheduling-based approaches. The experimental results validate the effectiveness of the proposed model in resource scheduling, financial risk analysis, and energy optimization. The intelligent model demonstrated significant improvements in managing end-to-end delays in wireless ad-hoc networks. In addition, the study showed that the proposed energy optimization methods effectively reduce energy consumption in storage systems by addressing both hardware and scheduling aspects. This research provides SMEs with a new intelligent management approach for financial technological innovation. It also offers practical solutions for energy optimization, contributing to sustainable computing practices. The findings can assist enterprises in improving efficiency and reducing operational costs through better resource management and energy use. The study’s applicability and effectiveness need further validation in larger-scale real-world environments due to experimental constraints. Future research should focus on extending the model’s application to diverse operational settings and conducting long-term performance assessments.