Semantic Segmentation-Based and Task-Aware Elastic Compression of Sequential Data for Aluminum Heating Furnaces

To address the challenges of compressing large-scale, multi-channel temperature data from aluminum alloy heating furnaces—and the limitations of traditional methods in preserving fidelity for critical tasks like energy accounting and process playback—this paper proposes an elastic, task-aware time-series compression method based on semantic segmentation. The method automatically segments data and annotates anchor points according to key process stages and significant operational events. Data are grouped by furnace number and alloy grade into segment-level buckets. Within this structure, an enhanced PCA model is built using channel-specific weights and amplified anchor points. The optimal principal component dimension is selected automatically under explained variance constraints, with channel-wise DCT used as a fallback for small samples. Compression accuracy is evaluated using combined rRMSE metrics (overall and per temperature channel) and key event recall rate. Experiments show the method achieves an average overall rRMSE of 0.11624, a temperature channel rRMSE of 0.08860, and a compression ratio of 1.18, outperforming Standard-PCA, PAA, and RP-Gauss. Notably, the proposed method achieves 100% recall for key events during heat preservation, demonstrating superior performance. Further analysis shows performance varies significantly across process stages, furnace IDs, and alloy grades, offering valuable insights for fine-grained evaluation and real-world deployment.