## 3 System Modeling
### Hierarchical Storage Structure  
In traditional power grids, a single medium is often used to store in situ data, whether it is sensor data, equipment operation information, maintenance records, or various types, sizes, and update frequencies of data, all stored in the same medium. As the volume of  
Figure 2: System Modeling Process. This figure provides a detailed illustration of the system modeling process, including in situ data generation, classification, storage, and access demand generation. data continues to grow, not only will the access costs increase, but storage efficiency and read speeds will gradually decrease, leading to slower data access and potentially affecting real-time data processing and decision-making. In the InS scenario, in situ data exhibits diverse types and rapid growth in scale. Traditional architectures struggle to allocate storage resources efficiently according to actual storage demands, resulting in suboptimal storage efficiency. Considering the heterogeneity of in situ data generated by distributed devices, this study focuses on three key storage features: static priority \(P\), storage size \(S\), and update frequency \(U\). Each piece of in situ data can be represented in the form of a triplet:  
\[\text{Data}=(P,S,U) \tag{1}\]  
where the static priority \(P\) distinguishes the priority of the in situ data and takes an integer value between 1 and 10. The storage size \(S\) indicates the current storage size of the in situ data and is a positive real number. The update frequency \(U\) represents the update frequency of the in situ data and is also a positive real number. According to the static priority \(P\) of each in situ data item, the data is preliminarily assigned to three types of servers with different performance levels. Two classification thresholds, \(P_{\text{min}}\) and \(P_{\text{max}}\), are derived from historical data analysis to reflect the actual demands associated with different priority levels. Data with priority values below \(P_{\text{min}}\) is preliminarily classified to the standard-performance server \(T_{\text{low}}\), those within the intermediate range are assigned to the medium-performance server \(T_{\text{medium}}\), and high-priority data with values greater than or equal to \(P_{\text{max}}\) is assigned to the high-performance server \(T_{\text{high}}\). \[T^{(0)}= \begin{cases}T_{low},&P<P_{\text{min}}\\ T_{medium},&P_{\text{min}}\leq P<P_{\text{max}}\\ T_{high},&P\geq P_{\text{max}}\end{cases}  \tag{2}\]  
Based on static priority classification, this study further introduces two key features of the data to refine and adjust the preliminary classification results. \(i\) denotes the specific category of servers, which can take values _low_, _medium_, or _high_. The system evaluates the overall matching degree between each piece of in situ data and the \(i\)-type server using a weighted similarity function. The scoring function considers two dimensions: storage size and update frequency, with the weights \(\omega_{S}\) and \(\omega_{U}\) controlling their relative importance in the total score. A higher score indicates that the data is more suitable, in terms of characteristics, to be assigned to the \(i\)-type server. The similarity-based classification result is denoted as \(T^{(1)}\):  
\[\text{Score}_{i}=\omega_{S}\cdot\phi_{S}^{i  
The standard deviation serves as a measure of deviation from the expected feature values, facilitating a more accurate characterization of the data's suitability for each server category, thereby enhancing the precision and robustness of classification decisions. The system ultimately performs a dynamic adjustment to the initial priority classification results based on the similarity scores. The decision logic for the classification process is expressed as follows:  
\[T= \begin{cases}T^{(1)},&\text{if }T^{(1)}\
eq T^{(0)}\text{ and }\left|\text{ Score}_{T^{(1)}}-\text{Score}_{T^{(0)}}\right|>\varepsilon\\ T^{(0)},&\text{otherwise}\end{cases}