## 2 Materials and Methods
### Datasets and Evaluation Metrics  
**Battery Charging Detection Data**. This dataset comes from Baidu PaddlePaddle Learning Competition: Energy AI Challenge--Anomaly Detection Track ([[https://aistudio.baidu.com/datasetdetail/168245](https://aistudio.baidu.com/datasetdetail/168245)]([https://aistudio.baidu.com/datasetdetail/168245](https://aistudio.baidu.com/datasetdetail/168245)), (accessed on 22 July 2024)). It consists of processed data from full vehicle charging segments, aiming to analyze whether a vehicle is currently experiencing any faults based on charging data. The competition selected data that contain actual battery faults, but where the traditional alarm system failed to provide early warning. The data represent a mix of multiple vehicles of the same model. The charging segment data are relatively stable in new energy vehicle battery data, and the parameter changes in the battery follow a regular pattern. It is important to note that these data have been cleaned, and the raw values no longer reflect the characteristics of the battery itself, but the trend of the data changes still follows the battery's regular patterns. The dataset is in.pkl file format, with each.pkl file containing a tuple (data, metadata). Each.pkl file corresponds to one battery and contains 28,390 samples. The shape of each data point is (256, 8), with each column representing different features as shown in Table 2. The metadata contains label and mileage information. The label \"00\" indicates normal, and \"10\" indicates abnormal. The mileage indicates the total distance for which the battery has been used. The proportion of abnormal samples is approximately 17%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Meaning** \\ \hline volt & Overall voltage \\ \hline current & Overall current \\ \hline soc & State of Charge \\ \hline max\_single\_volt & Maximum cell voltage \\ \hline min\_single\_volt & Minimum cell voltage \\ \hline max\_temp & Maximum temperature \\ \hline min\_temp & Minimum temperature \\ \hline timestamp & Timestamp \\ \hline \hline \end{tabular}  
* These features are used to monitor and detect battery performance during the charging process.  
\end{table} 
Table 2: Battery Charging Detection Data Features and Their Meanings. Figure 4: Architecture of the Neural Network-Based Weight Generation Function. **Fall Detection Data**. This dataset is a de-identified reconstruction of the dataset used in the paper by [PERSON] et al. [24], and is currently hosted on Kaggle ([[https://www.kaggle.com/datasets/jorekai/anomaly-detection-falling-people-events](https://www.kaggle.com/datasets/jorekai/anomaly-detection-falling-people-events)]([https://www.kaggle.com/datasets/jorekai/anomaly-detection-falling-people-events](https://www.kaggle.com/datasets/jorekai/anomaly-detection-falling-people-events)), (accessed on 4 November 2024)). The dataset consists of multiple CSV files, with each file recording the X, Y, Z position data of four sensors worn by different experimental subjects, along with time-point level anomaly labels, as shown in Table 3. Note that there is no timestamp feature in this dataset, as timestamps are often a key component in classification tasks. This can lead to an imbalance in learning tasks and limit the potential for broad generalization. The training set contains approximately 134,229 samples, with 5% of them being anomalous. The test set contains a total of 30,030 samples. **Motion State Recognition Data**. This dataset comes from the preliminary round of the Second National Embedded Software Development Competition hosted by VeriSilicon ([[https://bhpan.buaa.edu.cn/link/AA7C53](https://bhpan.buaa.edu.cn/link/AA7C53) FB7574F1434 DB72 FE002F6 DDB366]([https://bhpan.buaa.edu.cn/link/AA7C53](https://bhpan.buaa.edu.cn/link/AA7C53) FB7574F1434 DB72 FE002F6 DDB366), accessed on 15 December 2024).