louis
cfeedcf887
- 前馈冻结改用可配置的 3 秒流量绝对误差滑窗，修复采样抖动造成的重复解冻，并保留下游扰动后的自动更新 - 支持非单调阀特性反解、最小可测面积以下直接全关，以及闭阀端精细扫点与辨识开关 - 调整双压力判稳、最长等待时间、在线入口全开收尾和闭环四联图 - 更新配置、README、阀模型及离线测试，归档本轮实验数据与诊断产物
431 lines
15 KiB
Python
431 lines
15 KiB
Python
"""从开环扫点 CSV 拟合 A_eff(x) 表，输出 valve_model.json。
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用法：
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python identify_valve.py --csv open_loop_data/open_loop_xxx.csv --out valve_model.json
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python identify_valve.py --csv "open_loop_data/*.csv" --out valve_model.json --plot
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python identify_valve.py --plot # 省略 --csv 时默认合并 open_loop_data/ 下全部 CSV
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流程：
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1. --csv 支持多个路径与 glob 模式，全部 CSV 合并使用（文件名含时间戳，按名称
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排序即按实验先后合并）；省略时默认 open_loop_data/*.csv；
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2. 每个 CSV 按 step_index 分组，每组取末尾 --tail 个采样点平均，得到稳态
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(x, Q, P1, P2)，并按开度访问顺序标记逼近方向（上升/下降/该文件起始步）；
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3. 对每个稳态点反解 A_eff = Q / (P1_abs * F(r))，同一行程 x 的多个点
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（来自不同次实验）先平均再构表；
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4. 按行程升序构表，做单调性检查；
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5. 用 ValveModel 存成 JSON，并打印摘要（可选画诊断图，实测点按逼近方向着色）。
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"""
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import argparse
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import csv
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import glob
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import math
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import os
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from pathlib import Path
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import config
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from valve_model import CRITICAL_RATIO, ValveModel, abs_pressure, f_ratio
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DEFAULT_TAIL = 20
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# 逼近方向：按开度访问顺序，当前开度大于/小于上一开度。
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DIRECTION_UP = "up" # 从更小开度升到当前开度
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DIRECTION_DOWN = "down" # 从更大开度降到当前开度
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DIRECTION_ORDER = [DIRECTION_UP, DIRECTION_DOWN, None]
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DIRECTION_LABELS = {
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DIRECTION_UP: "ascending",
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DIRECTION_DOWN: "descending",
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None: "start (unknown)",
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}
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DIRECTION_COLORS = {
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DIRECTION_UP: "#1565C0",
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DIRECTION_DOWN: "#D84315",
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None: "#757575",
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}
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def _to_float(text):
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"""把 CSV 单元格解析成有限浮点数；空/非法/非有限返回 None。"""
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if text is None:
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return None
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text = str(text).strip()
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if text == "":
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return None
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try:
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value = float(text)
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except (TypeError, ValueError):
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return None
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return value if math.isfinite(value) else None
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def _average(values):
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"""返回非 None 数值的平均；没有有效值时返回 None。"""
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valid = [value for value in values if value is not None]
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return None if not valid else sum(valid) / len(valid)
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def load_steady_points(csv_path, tail=DEFAULT_TAIL):
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"""提取单个 CSV 每个 step_index 的稳态均值与逼近方向。
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返回按 step（访问顺序）排列的 ``(x, q, p1, p2, direction)`` 列表。
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direction 为 ``"up"``（开度上升逼近）/ ``"down"``（下降逼近）/
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``None``（该文件第一步，无上一开度可比较）。
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"""
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rows_by_step = {}
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opening_by_step = {}
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with open(csv_path, "r", newline="", encoding="utf-8-sig") as file:
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for row in csv.DictReader(file):
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step_text = (row.get("step_index") or "").strip()
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if step_text == "":
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continue
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try:
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step = int(float(step_text))
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except (TypeError, ValueError):
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continue
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rows_by_step.setdefault(step, []).append(row)
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if step not in opening_by_step:
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opening_by_step[step] = _to_float(row.get("opening_pct"))
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points = []
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prev_opening = None
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for step in sorted(rows_by_step):
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opening = opening_by_step.get(step)
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if prev_opening is not None and opening is not None:
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if opening > prev_opening:
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direction = DIRECTION_UP
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elif opening < prev_opening:
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direction = DIRECTION_DOWN
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else:
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direction = None
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else:
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direction = None
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if opening is not None:
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prev_opening = opening
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rows = rows_by_step[step]
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rows.sort(key=lambda r: _to_float(r.get("time_s")) or 0.0)
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tail_rows = rows[-tail:] if tail > 0 else rows
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x = _average(_to_float(r.get("motor_position")) for r in tail_rows)
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q = _average(_to_float(r.get("flow_after_slm")) for r in tail_rows)
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p1 = _average(_to_float(r.get("pressure_before_kpa")) for r in tail_rows)
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p2 = _average(_to_float(r.get("pressure_after_kpa")) for r in tail_rows)
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if None in (x, q, p1, p2):
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continue
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points.append((x, q, p1, p2, direction))
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return points
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def compute_area_table(steady_points, check_monotonic=True):
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"""把稳态点换算成 (x, A_eff) 表，并返回逐点测量值供诊断图着色。
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返回 ``(table, dropped, stats, area_points)``：
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- ``area_points``：每个实测点的 ``(x, A_eff, direction)``，按 x 升序，未平均；
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- ``table``：同一行程 x 的多个 A_eff 先平均；启用检查时再取最长单调连续段；
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- ``dropped``：因非单调被丢弃的 ``(x, A_eff)``；
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- ``stats``：阻塞/亚声速点统计（按原始测量点计数）。
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"""
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area_points = []
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choked = subsonic = 0
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for x, q, p1, p2, direction in steady_points:
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p1_abs = abs_pressure(p1)
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p2_abs = abs_pressure(p2)
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if p1_abs <= 0.0:
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continue
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r = p2_abs / p1_abs
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f = f_ratio(r)
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denom = p1_abs * f
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if denom <= 0.0 or q < 0.0:
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continue
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if r <= CRITICAL_RATIO:
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choked += 1
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else:
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subsonic += 1
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area_points.append((x, q / denom, direction))
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area_points.sort(key=lambda item: item[0])
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# 合并多份 CSV 后同一行程会有多个点：先按 x 分组平均（CSV 以 6 位小数
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# 记录行程，round 用于吸收极小浮点差异）。
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by_x = {}
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for x, a, _ in area_points:
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by_x.setdefault(round(x, 6), []).append(a)
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merged = [(float(x), sum(vals) / len(vals)) for x, vals in sorted(by_x.items())]
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ys = [a for _, a in merged]
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if not check_monotonic or _is_monotonic(ys):
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table = merged
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dropped = []
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else:
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start, end = _longest_monotonic_run(ys)
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table = merged[start:end + 1]
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dropped = merged[:start] + merged[end + 1:]
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stats = {"choked": choked, "subsonic": subsonic, "total": choked + subsonic}
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return table, dropped, stats, area_points
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def _is_monotonic(ys):
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"""序列是否单调（允许相等，但不允许中途反向）。"""
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direction = 0
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for i in range(1, len(ys)):
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delta = ys[i] - ys[i - 1]
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if delta == 0:
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continue
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sign = 1 if delta > 0 else -1
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if direction == 0:
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direction = sign
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elif direction != sign:
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return False
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return True
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def _longest_monotonic_run(ys):
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"""返回最长单调连续段的闭区间下标 ``(start, end)``。"""
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best_start = best_end = 0
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for start in range(len(ys)):
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direction = 0
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end = start
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for j in range(start + 1, len(ys)):
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delta = ys[j] - ys[j - 1]
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if delta == 0:
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end = j
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continue
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sign = 1 if delta > 0 else -1
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if direction == 0:
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direction = sign
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end = j
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elif direction == sign:
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end = j
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else:
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break
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if end - start > best_end - best_start:
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best_start, best_end = start, end
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return best_start, best_end
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def identify(csv_paths, tail=DEFAULT_TAIL, out_path=None):
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"""从一份或多份开环扫点 CSV 端到端辨识。
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返回 ``(model, steady_points, table, dropped, stats, area_points)``。
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``csv_paths`` 为路径列表，其稳态点合并后统一拟合。
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"""
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steady = []
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for path in csv_paths:
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steady.extend(load_steady_points(path, tail))
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check_monotonic = config.IDENTIFY_VALVE_MONOTONIC_CHECK_ENABLED
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table, dropped, stats, area_points = compute_area_table(
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steady,
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check_monotonic=check_monotonic,
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)
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if len(table) < 2:
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raise ValueError(
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f"有效稳态点不足（{len(table)} 个），无法构表。请确认 CSV 为 "
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"open_loop.py 产出（需含 step_index 列），且 "
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"pressure_before_kpa / pressure_after_kpa / flow_after_slm "
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"列读数合理。"
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)
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model = ValveModel(
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table,
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config.MOTOR_OPEN_POSITION,
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config.MOTOR_CLOSED_POSITION,
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enforce_monotonic=check_monotonic,
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)
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if out_path:
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model.save(out_path)
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return model, steady, table, dropped, stats, area_points
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def resolve_csv_paths(items=None):
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"""把 CLI 传入的路径/glob 展开为去重、按名称排序的 CSV 路径列表。
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``items`` 为空时默认使用工程目录下 ``open_loop_data/*.csv``（文件名含
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时间戳，排序后即按实验先后合并）。
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"""
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if not items:
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default_dir = Path(__file__).resolve().parent / "open_loop_data"
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items = [str(default_dir / "*.csv")]
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paths = []
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for item in items:
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pattern = str(item).replace("\\", "/")
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if glob.has_magic(pattern):
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paths.extend(sorted(glob.glob(pattern)))
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else:
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paths.append(pattern)
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# 去重：glob 结果与显式路径的斜杠方向可能不同，用 normcase 归一化比较
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# （Windows 下同时统一大小写）。
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unique = []
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seen = set()
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for path in paths:
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key = os.path.normcase(path)
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if key not in seen:
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seen.add(key)
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unique.append(path)
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return unique
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def _count_directions(steady_points):
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counts = {DIRECTION_UP: 0, DIRECTION_DOWN: 0, None: 0}
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for point in steady_points:
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counts[point[4]] += 1
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return counts
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def _scatter_by_direction(axis, points):
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"""按逼近方向分色画散点；``points`` 为 ``(x, y, direction)`` 列表。"""
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by_direction = {d: [] for d in DIRECTION_ORDER}
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for x, y, direction in points:
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by_direction.setdefault(direction, []).append((x, y))
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for direction in DIRECTION_ORDER:
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pts = by_direction.get(direction, [])
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if not pts:
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continue
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axis.scatter(
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[p[0] for p in pts],
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[p[1] for p in pts],
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color=DIRECTION_COLORS[direction],
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label=DIRECTION_LABELS[direction],
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s=18,
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alpha=0.85,
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)
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def create_diagnostic_plot(steady_points, table, dropped, area_points, image_path):
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"""画 x vs A_eff 与 x vs Q_ss 两张子图，实测点按逼近方向着色。
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同一行程下上升/下降两团点明显分离，说明阀门迟滞显著，前馈表应考虑
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分方向处理；完全重叠则迟滞可忽略。
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"""
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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xs = [x for x, _ in table]
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areas = [a for _, a in table]
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figure, axes = plt.subplots(2, 1, figsize=(10, 8), constrained_layout=True)
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figure.suptitle("Identified valve characteristic")
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axes[0].plot(xs, areas, color="#263238", linewidth=1.2, label="fitted A_eff (per-x mean)")
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_scatter_by_direction(axes[0], area_points)
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if dropped:
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axes[0].scatter(
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[x for x, _ in dropped],
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[a for _, a in dropped],
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color="#B71C1C",
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marker="x",
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label="dropped (non-monotonic)",
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)
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axes[0].set_ylabel("A_eff (slm/kPa)")
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axes[0].set_title("Effective area vs stroke")
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axes[0].legend(loc="best")
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axes[0].grid(True, alpha=0.3, linestyle="--")
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_scatter_by_direction(
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axes[1], [(p[0], p[1], p[4]) for p in steady_points]
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)
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axes[1].set_ylabel("Flow (SLM)")
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axes[1].set_xlabel("Motor stroke x")
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axes[1].set_title("Steady flow vs stroke")
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axes[1].legend(loc="best")
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axes[1].grid(True, alpha=0.3, linestyle="--")
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figure.savefig(image_path, dpi=config.PLOT_DPI, bbox_inches="tight")
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plt.close(figure)
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def parse_args(argv=None):
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parser = argparse.ArgumentParser(
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description="从开环扫点 CSV 拟合 A_eff(x) 阀特性表（支持多 CSV/glob 合并）"
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)
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parser.add_argument(
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"--csv", nargs="+",
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help="一个或多个 CSV 路径或 glob 模式；省略时默认合并 open_loop_data/*.csv",
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)
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parser.add_argument("--out", default="valve_model.json", help="输出 JSON 路径")
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parser.add_argument(
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"--tail", type=int, default=DEFAULT_TAIL,
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help=f"每组末尾用于平均的采样点数（默认 {DEFAULT_TAIL}）",
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)
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parser.add_argument("--plot", action="store_true", help="生成诊断图 PNG")
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args = parser.parse_args(argv)
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if args.tail < 1:
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parser.error("--tail 必须 >= 1")
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return args
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def main(argv=None):
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args = parse_args(argv)
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csv_paths = resolve_csv_paths(args.csv)
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if not csv_paths:
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print("未找到任何 CSV：请确认 open_loop_data/ 下有开环扫点数据，")
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print('或用 --csv 指定路径或 glob 模式（如 --csv "open_loop_data/*.csv"）。')
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return 2
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for path in csv_paths:
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if not Path(path).exists():
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print(f"CSV 不存在：{path}")
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return 2
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try:
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model, steady, table, dropped, stats, area_points = identify(
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csv_paths, tail=args.tail, out_path=args.out
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)
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except (ValueError, OSError) as exc:
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print(f"辨识失败：{exc}")
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return 2
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if len(csv_paths) <= 8:
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print("合并的 CSV：")
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for path in csv_paths:
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print(f" {path}")
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else:
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print(f"合并的 CSV：{csv_paths[0]} 等共 {len(csv_paths)} 个文件")
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directions = _count_directions(steady)
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print(
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f"读取稳态点 {len(steady)} 个：上升逼近 {directions[DIRECTION_UP]}、"
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f"下降逼近 {directions[DIRECTION_DOWN]}、起始点 {directions[None]}；"
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f"阻塞 {stats['choked']} 个，亚声速 {stats['subsonic']} 个，"
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f"合计 {stats['total']} 个。"
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)
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print(
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f"有效行程范围 {table[0][0]:.1f} ~ {table[-1][0]:.1f}"
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f"（{len(table)} 点，同一行程已平均）。"
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)
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if not config.IDENTIFY_VALVE_MONOTONIC_CHECK_ENABLED:
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print("提示：单调性检查已关闭，所有按行程平均后的点均被保留。")
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if dropped:
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print(
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"警告：A_eff(x) 非单调，以下点被丢弃"
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"（建议缩小扫点范围到单调段重扫）。"
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)
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print("行程 x -> A_eff：")
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for x, a in table:
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print(f" x={x:8.3f} A_eff={a:.6f}")
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if dropped:
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print("被丢弃的点：")
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for x, a in dropped:
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print(f" x={x:8.3f} A_eff={a:.6f} [DROPPED]")
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print(f"已保存阀特性模型：{args.out}")
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if args.plot:
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image_path = Path(args.out).with_suffix(".png")
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try:
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create_diagnostic_plot(steady, table, dropped, area_points, image_path)
|
||
print(f"诊断图已保存：{image_path}")
|
||
except Exception as exc:
|
||
print(f"生成诊断图失败（不影响 JSON）：{exc}")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import sys
|
||
|
||
sys.exit(main())
|