## 1 Introduction  
With the rapid advancement of internet and virtualization technologies, the cloud computing model based on remote servers has swiftly emerged. In 2023, the global cloud computing market reached a scale of USD 586.4 billion and is projected to exceed USD one trillion by 2027. The cloud computing paradigm is gradually replacing the traditional computing model that relies on local hardware resources, demonstrating strong development momentum [1]. According to Gartner's research, cloud computing can help enterprises reduce IT costs by 30% to 40%. Compared with locally hosted server architectures that require high maintenance costs, cloud-based computing models not only significantly reduce enterprises' infrastructure investments but also enhance data processing efficiency [2; 3]. The rise of cloud computing has provided strong support for data storage, processing, and management in terminal devices, and the Cloud-End System is gradually taking shape. According to Research and Markets studies, global IoT devices are projected to generate approximately 73.1 zettabytes of data by 2025, nearly a fourfold increase from 18.3 zettabytes in 2019. With the widespread deployment of terminal devices across various industrial systems, the volume of generated data is growing exponentially. However, the traditional approach of uploading massive amounts of data to the cloud for centralized processing is increasingly revealing a range of issues, including bandwidth bottlenecks, transmission latency, and excessive energy consumption [4; 5]. To mitigate these challenges, research trends are shifting toward processing data at the network edge, effectively reducing network load, shortening response time, and enhancing overall system processing efficiency [6]. To address these challenges, part of the computing and storage capabilities have been offloaded from centralized cloud servers to network edges closer to terminal devices, leading to the emergence of edge computing [7]. This advancement not only reduces data transmission latency but also enhances the system's real-time responsiveness and processing efficiency [5]. The Cloud-Edge-End System has been gradually refined, achieving efficient resource utilization and flexible data processing [6]. With the rapid development of the Cloud-Edge-End System, the number of IoT devices is expected to increase from 18.8 billion in 2025 to 40 billion by 2030. Consequently, various terminal devices, sensors, and user behaviors will generate massive volumes of data. In such a distributed scenario, data not only grows explosively in scale but also increases in variety and complexity, posing significant challenges to data storage, scheduling, and processing [8]. The vast amount of data flowing from the device end to the edge and then to the cloud leads to a sharp rise in data transmission frequency and volume, further exacerbating the burden on existing infrastructure [9]. The demand for in situ data processing is gradually emerging. In the smart grid, the locations of wind turbines are often remote, with factors such as power limitations and other constraints [10]. On the other hand, in the Cloud-Edge-End architecture, edge nodes are primarily deployed in areas with higher demand density [11]. When in situ data relies on the traditional Cloud-Edge-End architecture for storage and access, transmission delays and bandwidth limitations still pose significant challenges. In situ Server (InS) Systems address these limitations by deploying servers directly near the data sources, providing dedicated services to terminal devices with in situ data storage needs, thus forming a comprehensive InS system. Specifically, as shown in Table 1.  
In this study, to optimize the in situ data management scheme in the InS system, efficient storage and timely responses are achieved.