本项目实现了一个 CNN + BiLSTM 混合深度学习模型，用于 时间序列预测与回归任务（如电池充电曲线建模、健康状态估计等）。 数据处理：滑动窗口切片 + 归一化，自动生成训练/验证/测试集。 模型结构： CNN 提取局部特征 BiLSTM 捕捉双向时序依赖 全连接层输出预测结果 训练特性：早停机制、梯度裁剪、最佳模型权重保存 结果输出：支持 RMSE/MAE 评估，保存预测结果对比表格与可视化图表 该框架可作为 时间序列建模的基础模板，便于快速复现与扩展到不同场景。
☆31Sep 5, 2025Updated last year
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