feat(vd960Loop): 环境健康度算法 §10 + drift_health.py 参考实现
- variation-analysis.md §10: 无车漂移监测 (零成本, 不改协议) - 口径修正: 绝对峰=1.5×窗口漂移(Origin 滞后半窗) → 用峰谷差 - 600ms 采样系统性低估 ~12% (峰在窗口末采不到) - 冻结态失效(>4×dlt_ORG) → 双指标: 漂移速率 + 冻结占比 - 冻结判定双判据: 跨窗跳变消失 + 峰谷差突变(>4×前4窗中位数) - tools/drift_health.py: CSV 逐窗统计/冻结检测/JSON/自测 - devlog 2026-08-25 条目追加环境健康度小节 - 自测: 慢漂移 0.02%/s 识别准确, 冻结窗正确排除
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wangfq
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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drift_health.py — vd960Loop 无车漂移环境健康度分析（零成本）
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原理（docs/variation-analysis.md §10）:
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无车时 Origin 每 5s 阶跃更新（窗口均值）→ variation 呈锯齿
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锯齿峰谷差 = 窗口内漂移量 （绝对峰 = 1.5×窗口漂移, Origin 滞后半窗）
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漂移速率(%/s) = 峰谷差 / Origin / 窗口秒数 × 100
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输入: CSV (tick,origin,variation 三列) 或 --selftest 模拟数据
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输出: 逐窗口统计 + 汇总（平均速率/方向/冻结占比）
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注意:
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- 600ms 上报采样会使峰谷差系统性低估 ~12%（峰恒在窗口末, 末采样点错过）
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- 漂移 > 4×dlt_ORG 时基线冻结, 锯齿消失 → 冻结占比是第二指标
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- 冻结判定: 跨窗回落检测 (正常窗口边界回落≈峰谷差; 冻结继续同向爬升)
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用法:
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python3 drift_health.py --selftest
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python3 drift_health.py --csv drift.csv --window 500
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python3 drift_health.py --csv drift.csv --window 500 --json
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"""
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import argparse
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import csv
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import json
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import math
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import random
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import sys
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WINDOW_DEFAULT = 500 # tick, 5s @10ms
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TICK_S_DEFAULT = 0.01 # 10ms
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def analyze(variation, origins, window, tick_s):
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"""逐窗口统计。返回 (窗口列表, 汇总字典)"""
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import statistics
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n = len(variation)
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nw = n // window
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rows = []
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pkpk_hist = []
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for k in range(nw):
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a = k * window
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b = a + window
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seg = variation[a:b]
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oseg = origins[a:b]
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vmax = max(seg)
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vmin = min(seg)
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pkpk = vmax - vmin # 峰谷差 = 窗口漂移量
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origin = sum(oseg) / len(oseg)
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# 跨窗回落: jump = 下窗起点 - 本窗终点
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jump = None
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if k + 1 < nw:
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jump = variation[b] - variation[b - 1]
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# 冻结判定 (双判据):
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# ① 跨窗跳变消失: 正常 |jump| ≈ 峰谷差, 冻结 |jump| ≈ 0
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# ② 峰谷差突变: 当前窗 pkpk > 4× 前 4 窗中位数 (冻结=漂移持续累积)
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# 判据②让最后一窗(无 jump)也能判冻结, 实时滚动同样适用
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baseline = statistics.median(pkpk_hist[-4:]) if pkpk_hist else 0.0
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frozen = False
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if pkpk > 0:
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if jump is not None and abs(jump) < 0.3 * pkpk:
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frozen = True
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if baseline > 0 and pkpk > 4 * baseline:
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frozen = True
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pkpk_hist.append(pkpk)
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# 速率 %/s (按峰谷差)
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rate = pkpk / origin / (window * tick_s) * 100.0 if origin > 0 else 0.0
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rows.append({
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"window": k,
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"max": round(vmax, 4),
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"min": round(vmin, 4),
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"pkpk": round(pkpk, 4),
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"origin": round(origin, 1),
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"rate_pct_per_s": round(rate, 4),
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"jump": round(jump, 4) if jump is not None else None,
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"frozen": frozen,
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})
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if not rows:
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return [], {}
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rates = [r["rate_pct_per_s"] for r in rows if not r["frozen"]]
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frozen_cnt = sum(1 for r in rows if r["frozen"])
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summary = {
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"windows": len(rows),
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"frozen_ratio_pct": round(frozen_cnt / len(rows) * 100, 1),
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"avg_rate_pct_per_s": round(sum(rates) / len(rates), 4) if rates else None,
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"max_rate_pct_per_s": round(max(rates), 4) if rates else None,
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"last_direction": "正(频率上升/CAPVD下降)" if rows[-1]["pkpk"] > 0 and rows[-1]["max"] >= abs(rows[-1]["min"]) else "负(频率下降/CAPVD上升)",
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}
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return rows, summary
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def gen_selftest(window=WINDOW_DEFAULT, tick_s=TICK_S_DEFAULT):
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"""生成模拟数据: 前 6 窗慢漂移(0.02%/s, 正常锯齿), 后 2 窗快漂移(0.5%/s, 冻结)
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归一化坐标: Origin≈1.0, 漂移为相对比例"""
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r_slow = 2e-6 # 0.02%/s → 窗口漂移 0.1%
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r_fast = 5e-5 # 0.5%/s → 窗口漂移 2.5%, 远超 4×dlt_ORG → 冻结
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n = 8 * window
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capvd = []
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for i in range(n):
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if i < 6 * window:
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capvd.append(1.0 + i * r_slow)
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else:
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capvd.append(1.0 + 6 * window * r_slow + (i - 6 * window) * r_fast)
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# Origin: 窗口 k 期间 = 窗口 k-1 均值 (k>=1); 冻结窗(6,7) 锁在窗口 5 均值
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origin = [0.0] * n
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base6 = sum(capvd[5 * window:6 * window]) / window
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for i in range(n):
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k = i // window
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if k == 0:
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origin[i] = capvd[0]
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elif k < 6:
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origin[i] = sum(capvd[(k - 1) * window:k * window]) / window
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else:
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origin[i] = base6
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variation = [origin[i] - capvd[i] for i in range(n)]
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return variation, origin
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def fmt_table(rows, summary):
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lines = []
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lines.append(f"{'窗口':<5}{'max':>10}{'min':>10}{'峰谷差':>10}{'Origin':>12}{'速率%/s':>10}{'跨窗jump':>10} 状态")
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for r in rows:
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state = "冻结" if r["frozen"] else "正常"
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jump = f"{r['jump']:+.4f}" if r["jump"] is not None else " -"
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lines.append(f"{r['window']:<5}{r['max']:>10.4f}{r['min']:>10.4f}{r['pkpk']:>10.4f}"
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f"{r['origin']:>12.1f}{r['rate_pct_per_s']:>10.4f}{jump:>10} {state}")
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lines.append("-" * 66)
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lines.append(f"平均速率: {summary['avg_rate_pct_per_s']} %/s "
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f"最大速率: {summary['max_rate_pct_per_s']} %/s "
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f"冻结占比: {summary['frozen_ratio_pct']}% "
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f"末窗方向: {summary['last_direction']}")
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return "\n".join(lines)
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def main():
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p = argparse.ArgumentParser(
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description="vd960Loop 无车漂移环境健康度分析（零成本, 用现有 variation 锯齿）",
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formatter_class=argparse.RawDescriptionHelpFormatter, epilog=__doc__)
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p.add_argument("--csv", help="CSV 文件, 表头含 tick,origin,variation")
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p.add_argument("--window", type=int, default=WINDOW_DEFAULT, help=f"窗口 tick (默认 {WINDOW_DEFAULT}=5s)")
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p.add_argument("--tick-s", type=float, default=TICK_S_DEFAULT, help=f"tick 秒数 (默认 {TICK_S_DEFAULT}=10ms)")
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p.add_argument("--json", action="store_true", help="JSON 输出")
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p.add_argument("--selftest", action="store_true", help="模拟数据自测")
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args = p.parse_args()
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if args.selftest:
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variation, origin = gen_selftest(args.window, args.tick_s)
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rows, summary = analyze(variation, origin, args.window, args.tick_s)
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if args.json:
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print(json.dumps({"rows": rows, "summary": summary}, ensure_ascii=False, indent=2))
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else:
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print("模拟: 前 6 窗慢漂移 r=0.0002/tick (0.1%/窗 → 0.02%/s), 后 2 窗快漂移 r=0.002 (冻结)")
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print(fmt_table(rows, summary))
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return
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if not args.csv:
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p.error("需要 --csv 或 --selftest")
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variations, origins = [], []
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with open(args.csv, newline="", encoding="utf-8") as f:
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for row in csv.DictReader(f):
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variations.append(float(row["variation"]))
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origins.append(float(row["origin"]))
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if len(variations) < args.window:
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p.error(f"数据不足一个窗口 ({len(variations)} < {args.window})")
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rows, summary = analyze(variations, origins, args.window, args.tick_s)
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if args.json:
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print(json.dumps({"rows": rows, "summary": summary}, ensure_ascii=False, indent=2))
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else:
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print(fmt_table(rows, summary))
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if __name__ == "__main__":
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sys.exit(main())
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