## 5 Conclusions  
We propose a novel multi-task temporal modeling framework, BiCA-LI, which integrates LSTM and Informer encoders with a bidirectional cross-attention mechanism to achieve precise forecasting and anomaly detection for critical metrics in Internet Data Center (IDC) equipment environments. The model not only captures both short-term dynamics and long-term dependencies simultaneously but also effectively fuses local and global temporal contexts through the bidirectional cross-attention module, enhancing its ability to perceive complex sequential patterns. Building on this, an uncertainty-aware loss weighting strategy is introduced to further improve the optimization balance in multi-task learning, mitigating task interference commonly observed in traditional hard parameter-sharing architectures. This innovation effectively promotes stable convergence and generalization performance.  
Empirical results demonstrate that BiCA-LI significantly outperforms conventional LSTM and Informer models in both forecasting accuracy and anomaly detection capability. In regression tasks, the model achieves a mean absolute error (MAE) of 0.086, reflecting its robust capacity to capture subtle fluctuations in power and environmental metrics. For classification tasks, it attains 100% precision and 99.5% accuracy, highlighting its potential for reliable fault identification in high-availability scenarios. Unlike most existing approaches that rely on serial or task-decoupled modeling, BiCA-LI provides an end-to-end, unified solution that maintains individual task performance while enabling contextual information sharing across tasks, offering substantial practical applicability.  
Although the model has demonstrated superior performance on real-world IDC data, its generalization capabilities in other domains require further validation. Current experiments primarily focus on IDC equipment environments, and the baseline models selected for comparison are limited to structures related to its modular components. Future work may explore multiple directions: (1) further evaluating its robustness in open production environments and cross-domain tasks; (2) conducting horizontal comparisons across different model architectures using publicly available datasets to investigate their response patterns to input variations; and (3) addressing the issue of high model complexity by researching structural optimization and lightweight deployment techniques to meet resource constraints in edge computing scenarios.  
In conclusion, BiCA-LI offers an efficient, robust, and scalable solution for temporal modeling in high-energy-consumption IDC environments. It establishes a solid foundation for deploying multi-task learning in industrial intelligent maintenance systems, with broad prospects for extension to diverse real-world applications.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15137168/s1](https://www.mdpi.com/article/10.3390/app15137168/s1)]([https://www.mdpi.com/article/10.3390/app15137168/s1](https://www.mdpi.com/article/10.3390/app15137168/s1)). Supplementary materials are provided with this research, including training dataset.  
Conceptualization, X.Z.; methodology, Z.S. and Y.Z.; software, Y.Z.; validation, Z.S., Z.G., and X.Z.; formal analysis, Z.S. and Z.G.; resources, C.W. and X.Z.; data curation, Z.S. and Z.C.; writing--original draft, Z.S.; writing--review and editing, Z.S.,Y.Z., and X.Z.; visualization, Z.S. and Z.C.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by National Natural Science Foundation of China 52475495, National Natural Science Foundation of China U21A20122, and Zhejiang Provincial Natural Science Foundation LD24E050003.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.