Adaptive Data Compression Algorithm of Consumption Data Based on Cloud-Edge Collaboration and Q-Learning

With the advancement of the new-type power system, the exponentially growing high-frequency distribution and consumption data imposes heavy transmission and processing pressure on resource-constrained edge devices. Existing compression methods face two core limitations: static algorithm configurations that fail to adapt to dynamic time-varying power load characteristics, and complex computations that are difficult to deploy on resource-constrained edge terminals. To address these issues, this paper proposes a cloud-edge collaborative adaptive compression method based on CNN-LSTM load forecasting and Q-learning decision-making. A three-layer “cloud-edge-terminal” architecture is built to decouple compression decision-making from edge execution. The cloud employs a hybrid one-dimensional CNN and single-layer LSTM (1D-CNN-LSTM) for high-precision short-term load forecasting, and establishes an adaptive Q-learning decision mechanism to issue differentiated compression instructions according to varying load characteristics. The edge terminals receive these instructions and perform lightweight lossless compression accordingly. Simulation results show that the CNN-LSTM model achieves a MAPE of 7.59%. The Q-learning agent converges to an average reward of 65.35% during training and achieves a 66.73% overall compression ratio on the unseen test set, outperforming the fixed LZW baseline by approximately 6 percentage points. Furthermore, the proposed method improves the edge processing throughput by approximately 6.5 to 10.4 times compared to the comparative baselines. These results suggest that the cloud-edge collaborative approach offers a promising direction for alleviating edge pressure and balancing compression efficiency with computational overhead in massive power data transmission scenarios.