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import os
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import pickle
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import glob
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def load_and_merge_pickle_chunks(folder_path, file_pattern="*.pkl"):
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"""
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从指定文件夹中读取所有匹配的分片文件，解包并合并成一个总的数据列表。
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Args:
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folder_path: 存放 .pkl 分片文件的文件夹路径
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file_pattern: 文件匹配模式，默认匹配所有 .pkl 文件
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"""
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all_episodes = []
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# 获取所有匹配的 pkl 文件路径，并按名称排序（确保 part1, part2 顺序或逻辑清晰）
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search_path = os.path.join(folder_path, file_pattern)
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file_list = sorted(glob.glob(search_path))
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if not file_list:
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print(f"❌ 未在路径 【{folder_path}】 下找到任何匹配 【{file_pattern}】 的文件！")
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return []
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print(f"📂 找到 {len(file_list)} 个数据分片文件，开始加载...")
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for file_path in file_list:
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try:
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with open(file_path, 'rb') as f:
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# 每个分片解包出来都是一个 list [ep1, ep2, ...]
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chunk_data = pickle.load(f)
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if isinstance(chunk_data, list):
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all_episodes.extend(chunk_data)
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print(f" ✅ 成功加载: {os.path.basename(file_path)} (包含 {len(chunk_data)} 个 Episode)")
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else:
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print(f" ⚠️ 警告: {os.path.basename(file_path)} 解析出的数据格式不是列表，跳过。")
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except Exception as e:
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print(f" ❌ 读取文件 {os.path.basename(file_path)} 失败: {e}")
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print(f"整个序列加载完成，共合并了 {len(all_episodes)} 个 Episode。")
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return all_episodes
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def analyze_episodes_data(episode_data_raw):
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"""
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分析 Episode 数据，统计超调情况。
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"""
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total_episodes = len(episode_data_raw)
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if total_episodes == 0:
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print("没有数据可供分析。")
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return
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invalid_count = 0 # 最后一步误差绝对值 > 2 kPa 的无效 episode
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invalid_high_flow = 0 # 无效 episode 中流量 > 200
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invalid_low_flow = 0 # 无效 episode 中流量 < 100
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all_steady_abs_errors = [] # 所有有效 episode 的稳态误差（绝对值）
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no_overshoot_count = 0
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no_overshoot_abs_errors = [] # 绝对值稳态误差
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no_overshoot_raw_errors = [] # 带符号稳态误差（+ = 高于目标, - = 低于目标）
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overshoot_lt_1_count = 0
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overshoot_1_to_2_count = 0
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overshoot_2_to_3_count = 0
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overshoot_3_to_4_count = 0
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overshoot_4_to_5_count = 0
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overshoot_5_to_10_count = 0
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overshoot_gt_10_count = 0
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overshoots_5_to_10 = []
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overshoots_gt_10 = []
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for idx, ep in enumerate(episode_data_raw):
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pressures = ep.get('pressures', [])
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target_p = ep.get('target_pressure', 0.0)
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if not pressures:
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continue
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# 最后一步误差绝对值 > 2 kPa → 无效 episode，跳过
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errors = ep.get('errors', [])
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if errors and abs(errors[-1]) > 2:
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invalid_count += 1
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q = ep.get('Q_in', 0)
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if q > 200:
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invalid_high_flow += 1
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elif q < 100:
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invalid_low_flow += 1
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continue
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initial_p = pressures[0]
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# 所有有效 episode 的稳态误差（最后 30 步绝对值均值）
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if errors:
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last_n = errors[-30:] if len(errors) >= 30 else errors
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all_steady_abs_errors.append(sum(abs(e) for e in last_n) / len(last_n))
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is_step_up = target_p >= initial_p # 升压为 True，降压为 False
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overshoot = 0.0
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if is_step_up:
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# 升压：最大值大于目标压力为超调
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max_p = max(pressures)
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if max_p > target_p:
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overshoot = max_p - target_p
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else:
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# 降压：最小值小于目标压力为超调
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min_p = min(pressures)
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if min_p < target_p:
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overshoot = target_p - min_p
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# 统计区间
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if overshoot == 0:
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no_overshoot_count += 1
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elif overshoot < 1.0:
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overshoot_lt_1_count += 1
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# 最后 30 步的平均误差作为稳态误差（分别记录绝对值和带符号值）
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if len(errors) >= 30:
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last_30 = errors[-30:]
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elif errors:
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last_30 = errors
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else:
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last_30 = []
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if last_30:
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no_overshoot_abs_errors.append(sum(abs(e) for e in last_30) / len(last_30))
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no_overshoot_raw_errors.append(sum(last_30) / len(last_30))
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elif 1.0 <= overshoot < 2.0:
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overshoot_1_to_2_count += 1
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elif 2.0 <= overshoot < 3.0:
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overshoot_2_to_3_count += 1
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elif 3.0 <= overshoot < 4.0:
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overshoot_3_to_4_count += 1
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elif 4.0 <= overshoot <= 5.0:
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overshoot_4_to_5_count += 1
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else:
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item = {
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"index": idx,
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"direction": "升压" if is_step_up else "降压",
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"initial_p": initial_p,
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"target_p": target_p,
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"overshoot_value": round(overshoot, 3),
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"Q_in": ep.get("Q_in", 0),
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}
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if overshoot <= 10.0:
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overshoot_5_to_10_count += 1
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overshoots_5_to_10.append(item)
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else:
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overshoot_gt_10_count += 1
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overshoots_gt_10.append(item)
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# 打印报告
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def _pct(n): return f"{n / total_episodes * 100:.1f}%"
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print("\n" + "="*25 + " 离线数据分析 " + "="*25)
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valid_episodes = total_episodes - invalid_count
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print(f"合并后的总 Episode 数 : {total_episodes}")
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print(f" - 无效 Episode（末步误差>2）: {invalid_count} ({_pct(invalid_count)})")
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if invalid_count > 0:
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print(f" ├ 流量 > 200 L/min : {invalid_high_flow}")
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print(f" └ 流量 < 100 L/min : {invalid_low_flow}")
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print(f" - 有效 Episode 数 : {valid_episodes}")
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print(f" - 未超调的 Episode 数 : {no_overshoot_count} ({_pct(no_overshoot_count)})")
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print(f" - 超调 < 1 kPa : {overshoot_lt_1_count} ({_pct(overshoot_lt_1_count)})")
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print(f" - 超调在 1 ~ 2 kPa 之间 : {overshoot_1_to_2_count} ({_pct(overshoot_1_to_2_count)})")
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print(f" - 超调在 2 ~ 3 kPa 之间 : {overshoot_2_to_3_count} ({_pct(overshoot_2_to_3_count)})")
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print(f" - 超调在 3 ~ 4 kPa 之间 : {overshoot_3_to_4_count} ({_pct(overshoot_3_to_4_count)})")
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print(f" - 超调在 4 ~ 5 kPa 之间 : {overshoot_4_to_5_count} ({_pct(overshoot_4_to_5_count)})")
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print(f" - 超调在 5 ~ 10 kPa 之间 : {overshoot_5_to_10_count} ({_pct(overshoot_5_to_10_count)})")
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print(f" - 超调 > 10 kPa : {overshoot_gt_10_count} ({_pct(overshoot_gt_10_count)})")
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print("=" * 68)
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def _print_detail(title, items):
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if items:
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print(f"\n[⚠️ {title}]:")
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for item in items:
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print(f" * Episode [{item['index']}] ({item['direction']}): "
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f"初始 {item['initial_p']:.2f} -> 目标 {item['target_p']:.2f} | "
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f"超调量: {item['overshoot_value']:.2f} kPa | "
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f"流量: {item['Q_in']:.1f} L/min")
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_print_detail("超调在 5 ~ 10 kPa", overshoots_5_to_10)
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_print_detail("超调大于 10 kPa", overshoots_gt_10)
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if not overshoots_5_to_10 and not overshoots_gt_10:
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print("\n🎉 极好！没有发现超调大于 5 kPa 的数据。")
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# ---- 流量分布统计 ----
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flow_bins = [
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(0, 10), (10, 50), (50, 100), (100, 150),
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(150, 200), (200, 250), (250, 300),
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]
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flow_counts = {f"{lo}~{hi}": 0 for lo, hi in flow_bins}
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flow_counts["300+"] = 0
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for ep in episode_data_raw:
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q = ep.get('Q_in', 0)
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placed = False
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for lo, hi in flow_bins:
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if lo <= q < hi:
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flow_counts[f"{lo}~{hi}"] += 1
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placed = True
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break
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if not placed:
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flow_counts["300+"] += 1
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print(f"\n📊 流量分布统计 (共 {total_episodes} 个 Episode):")
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for lo, hi in flow_bins:
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label = f"{lo}~{hi}"
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print(f" {label:>10} L/min : {flow_counts[label]:>5} ({flow_counts[label]/total_episodes*100:5.1f}%)")
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print(f" {'300+':>10} L/min : {flow_counts['300+']:>5} ({flow_counts['300+']/total_episodes*100:5.1f}%)")
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if all_steady_abs_errors:
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avg_all = sum(all_steady_abs_errors) / len(all_steady_abs_errors)
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print(f"\n📊 所有有效 Episode 平均稳态误差（最后 30 步绝对值均值）: {avg_all:.3f} kPa"
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f" ({len(all_steady_abs_errors)} 个 Episode)")
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if no_overshoot_abs_errors:
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avg_abs = sum(no_overshoot_abs_errors) / len(no_overshoot_abs_errors)
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avg_raw = sum(no_overshoot_raw_errors) / len(no_overshoot_raw_errors)
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print(f"\n📊 超调0～1kpa Episode 平均稳态误差（最后 30 步）:")
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print(f" 绝对值均值 : {avg_abs:.3f} kPa")
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print(f" 带符号均值 : {avg_raw:.3f} kPa ({'偏高于目标' if avg_raw > 0 else '偏低' if avg_raw < 0 else '无偏'})"
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f" ({no_overshoot_count} 个 Episode)")
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def print_episode_detail(episode_data_raw, index):
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"""打印指定 episode 的完整数据"""
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if index < 0 or index >= len(episode_data_raw):
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print(f"❌ Episode 索引 {index} 超出范围 (0~{len(episode_data_raw)-1})")
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return
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ep = episode_data_raw[index]
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print(f"\n{'='*60}")
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print(f" Episode [{index}] 完整数据")
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print(f"{'='*60}")
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for key in ['Q_in', 'volume', 'target_pressure', 'mode']:
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if key in ep:
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print(f" {key}: {ep[key]}")
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pressures = ep.get('pressures', [])
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errors = ep.get('errors', [])
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valve_openings = ep.get('valves', [])
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print(f"\n 步数: {len(pressures)}")
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if pressures:
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print(f" 初始压力: {pressures[0]:.2f} kPa")
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print(f" 最终压力: {pressures[-1]:.2f} kPa")
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print(f" 目标压力: {ep.get('target_pressure', 'N/A')} kPa")
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if errors:
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print(f" 最终误差: {errors[-1]:.3f} kPa")
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print(f"\n {'步':>4s} {'压力(kPa)':>10s} {'误差(kPa)':>10s} {'开度(%)':>8s}")
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print(f" {'-'*36}")
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n = len(pressures)
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for i in range(n):
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p = pressures[i]
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e = errors[i] if i < len(errors) else float('nan')
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vo = valve_openings[i] if i < len(valve_openings) else float('nan')
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print(f" {i:4d} {p:10.2f} {e:10.3f} {vo:8.2f}")
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print(f"{'='*60}\n")
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# --- 执行离线分析 ---
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if __name__ == "__main__":
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# 💡 数据存放文件夹路径
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DATA_FOLDER = "/Users/menglingrui/Documents/DominatedConvergence/cloud_down_file/永久/data_8L"
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# 1. 读取并合并分片
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merged_data = load_and_merge_pickle_chunks(DATA_FOLDER, file_pattern="*part*.pkl")
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# 2. 执行分析
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if merged_data:
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analyze_episodes_data(merged_data)
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# 3. 找出无效 episode（末步误差绝对值 > 2 kPa），打印前 3 个的完整数据
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# invalid_indices = []
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# for idx, ep in enumerate(merged_data):
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# errors = ep.get('errors', [])
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# if errors and abs(errors[-1]) > 2:
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# invalid_indices.append(idx)
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# if len(invalid_indices) >= 3:
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# break
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# if invalid_indices:
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# print(f"\n找到 {len(invalid_indices)} 个无效 Episode，索引: {invalid_indices}")
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# for idx in invalid_indices:
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# print_episode_detail(merged_data, idx)
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# else:
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# print("\n未找到无效 Episode")
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print_episode_detail(merged_data, 2500)
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