## 1 Introduction
The specific environment and management architecture are shown in Figure 1. The main contributions of this paper are as follows:  
* Design of a hierarchical storage structure: A classification mechanism is designed to preprocess data with different attributes based on the diverse characteristics of in situ data, and store it in server clusters at different hierarchical levels to enhance system access efficiency. * Design of personalized demand queues: When access demands for in situ data from different hierarchical levels arise, they are received and recorded through personalized demand queues, ensuring timely responses to demands from each level. * Setting up an algorithm-driven demand response mechanism: For access demands in different personalized demand queues, the Double Deep Q-Network with Prioritized Experience Replay algorithm is introduced to assist in decision-making and response. The structure of this paper is as follows: Section 1 introduces the technical background of in situ data management in the InS system within the smart grid. Section 2 discusses the development of smart grid technologies, the application of in situ data and its concepts across multiple domains, and the application of reinforcement learning in the data field. Section 3 introduces the relevant architecture for in situ data management in the InS system and the evaluation metrics for assessing system management schemes. Section 4 presents the DDQN algorithm upon which the management strategy is based. Section 5 details \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Aspect** & **Cloud-Edge-End Architecture** & **InS System** \\ \hline Applicable Scenarios & General-purpose terminals and large-scale application scenarios & Terminal systems requiring in situ data processing \\ \hline Data Processing & Data is transmitted to remote edge nodes and then uploaded to the cloud are considered only when necessary \\ \hline Data Generation Location & Terminal data generated near urban areas & Terminal data generated near wind turbines \\ \hline End Node Characteristics & Distributed in urban areas with higher computing demand density & Distributed in remote areas due to constraints such as wind conditions and geographic limitations \\ \hline Edge Nodes Location & Edge nodes are primarily deployed in areas with higher demand density & Edge nodes are primarily deployed in areas with higher demand density \\ \hline Transmission to Edge Nodes & Relatively short transmission distance & Relatively long transmission distance, significantly constrained by latency and bandwidth limitations \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison between traditional cloud-edge-end architecture and InS system. This table compares the traditional cloud-edge-end architecture with the InS system across various aspects. Figure 1: InS System and in situ data management architecture. This figure illustrates the architecture of the in situ server system (InS) in a smart grid environment, providing a comprehensive depiction of the overall in situ data management process. It primarily consists of a hierarchical storage structure and personalized demand queues. When terminal devices generate in situ data, the system first classifies the data based on its characteristics and stores it in different server clusters within the hierarchical structure. Subsequently, in response to the generated access demands, the system distributes them into the corresponding queues within the personalized demand queues to support differentiated response strategies. the experimental simulation process and comparative results of the management strategy. Section 6 provides a comprehensive summary of the research.