wangfq
91ed16a9b8
- 图示 docs/img/timing-chain-pd132t.{svg,png}：① 一个窗（0~11.2ms）② 检测到输出（0~600ms）③ 基线块平均（0~12s）
· 曲线按 §1~§4 公式真算：Xn=480 tick/160µs、窗=68×480/3MHz=10.880ms、α=79(τ=35.26ms)、
dlt=(32640×60)>>16=29、判据线 32611、大信号(+0.5%) 第 1 窗即越线(t≈10.9ms)、小信号(+0.1%) ≈2.2τ≈78ms
· 同时标注：两种检测路径、50ms 输出节拍量化、上电时 Origin 只有首窗单点样本
- 新工具 tools/plot_timing_chain.py：纯 stdlib 手写 SVG；chromium --headless 转 PNG（零第三方依赖）
- ⚠ 新增 §0.1 符号约定：车辆 ⇒ L↓ ⇒ f↑ ⇒ 周期/Xn↓ ⇒ Value/CAPVD↓（判据取下降侧）
⇒ 修正 5 处「深占压（频偏 −5%）⇒ 窗 +5.3%」的方向错误：车辆是频率升高、窗长应【缩短】(−4.8%)
- README：目录树补 img/ 与两个 tools 脚本；表格校验 0 异常（15 文件）
279 lines
15 KiB
Python
279 lines
15 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""端到端时序图生成器（PD132T 主图，PD136HA 对照）—— 纯 stdlib 手写 SVG
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输出：docs/img/timing-chain-pd132t.svg
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转 PNG（仓库外，供 IM 预览）：
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chromium --headless=new --disable-gpu --no-sandbox \
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--screenshot=out.png --window-size=1680,1320 --default-background-color=FFFFFFFF \
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file:///abs/path/timing-chain-pd132t.svg
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图中所有曲线都是按 §9 的公式真算的，不是示意画：
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窗结账 = LPCNT 个 Xn 之和；CAPVD 为 79/256 一阶 IIR；判据 = CAPVD < Origin − floor(Origin·表值/65536)
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"""
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import math
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# ---------- 站点参数（PD132T @ 100kHz） ----------
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F_CAP = 3.0e6 # 捕获时钟 Hz（官方手册直证：SYSCLK/4）
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F_COIL = 100e3 # 线圈频率 Hz（站点）
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DIVIDER = 32 # CD4060 ÷32（Q5）
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EDGE = 2 # 双沿捕获（CCAPM1=0x31）
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BASE = 32768
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SENS_ENTER = 60 # 档1 进入表值（/65536）
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ALFA = 79 # Flt_Reg（FLT 拨码 OFF）
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DF_REL = 0.5e-3 # 车辆引起的相对频偏（0.5%，实际车型量级）
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XN0 = round(DIVIDER * F_CAP / (EDGE * F_COIL)) # 480 tick @100kHz
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OUT = 'docs/img/timing-chain-pd132t.svg'
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LPCNT = BASE // XN0
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WIN_TICKS = LPCNT * XN0
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T_XN = XN0 / F_CAP
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T_WIN = WIN_TICKS / F_CAP
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TAU = T_WIN * 256.0 / ALFA
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ORIGIN = WIN_TICKS
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DLT = (ORIGIN * SENS_ENTER) >> 16
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XN1 = round(XN0 / (1 - DF_REL))
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VALUE_CAR = LPCNT * XN1
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T_ENTER_TRUE = 0.6e-3 # 车辆真实进入时刻（窗内）
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W, H = 1680, 1560
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P = [] # SVG 片段
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def add(s): P.append(s)
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def esc(t): return t.replace('&', '&').replace('<', '<').replace('>', '>')
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def txt(x, y, s, size=15, fill='#111', anchor='start', weight='normal', fam='Noto Serif CJK SC, Noto Sans CJK SC, sans-serif'):
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add('<text x="%.1f" y="%.1f" font-family="%s" font-size="%d" fill="%s" text-anchor="%s" font-weight="%s">%s</text>'
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% (x, y, fam, size, fill, anchor, weight, esc(s)))
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def line(x1, y1, x2, y2, stroke='#111', w=1.4, dash=None, op=1.0):
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d = ' stroke-dasharray="%s"' % dash if dash else ''
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add('<line x1="%.1f" y1="%.1f" x2="%.1f" y2="%.1f" stroke="%s" stroke-width="%.1f" opacity="%.2f"%s/>'
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% (x1, y1, x2, y2, stroke, w, op, d))
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def poly(pts, stroke='#0a58ca', w=1.8, fill='none', op=1.0):
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s = ' '.join('%.2f,%.2f' % p for p in pts)
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add('<polyline points="%s" fill="%s" stroke="%s" stroke-width="%.1f" opacity="%.2f" stroke-linejoin="round"/>'
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% (s, fill, stroke, w, op))
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def rect(x, y, w_, h_, fill='#fff', stroke='none', rx=4, op=1.0, sw=1.2):
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add('<rect x="%.1f" y="%.1f" width="%.1f" height="%.1f" rx="%d" fill="%s" stroke="%s" stroke-width="%.1f" opacity="%.2f"/>'
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% (x, y, w_, h_, rx, fill, stroke, sw, op))
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def dot(x, y, r=3.0, fill='#c1121f'):
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add('<circle cx="%.1f" cy="%.1f" r="%.1f" fill="%s"/>' % (x, y, r, fill))
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def note(x, y, w_, h_, title, lines_, accent='#0a58ca'):
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rect(x, y, w_, h_, fill='#fbfcfe', stroke=accent, rx=6, sw=1.2)
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txt(x + 12, y + 24, title, 16, accent, weight='bold')
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for i, s in enumerate(lines_):
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txt(x + 12, y + 48 + i * 21, s, 14, '#333')
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# ================= 真算：窗序列 / CAPVD / 判据 =================
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def sim(df_rel, n_windows=60):
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"""df_rel: 车辆引起的相对频升（>0）。返回 (t_s, Value, CAPVD) 逐窗列表。"""
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f1 = F_COIL * (1 + df_rel)
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xn1 = DIVIDER * F_CAP / (EDGE * f1)
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value_car = LPCNT * xn1
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thr = ORIGIN - DLT
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out, capvd = [], float(ORIGIN)
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for n in range(n_windows):
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frac = 1.0 if n >= 1 else max(0.0, (T_WIN - T_ENTER_TRUE) / T_WIN)
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v = WIN_TICKS + frac * (value_car - WIN_TICKS)
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capvd = capvd + (v - capvd) * ALFA / 256.0
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out.append(((n + 1) * T_WIN, v, capvd))
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cross = next((i for i, (_, _, c) in enumerate(out) if c < thr), None)
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return out, thr, (None if cross is None else (cross + 1) * T_WIN)
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big, THR, T_CROSS = sim(0.005) # 大信号 Δf/f = +0.5%
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sml, _, T_CROSS_S = sim(0.001) # 小信号 Δf/f = +0.1%
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# ================= 画布与坐标 =================
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X0, X1 = 250.0, 1470.0
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add('<rect width="%d" height="%d" fill="#ffffff"/>' % (W, H))
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txt(28, 44, 'PD132T 端到端时序图（检测通道：线圈 → CD4060 ÷32 → PCA 捕获 → 窗结账 → IIR → 基线 → 继电器）', 21, '#0b2c4d', weight='bold')
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txt(28, 70, '站点：线圈 100 kHz / 捕获时钟 3 MHz（12MHz÷4，官方手册直证）/ Xn₀=480 tick / LPCNT=68 / 窗=32640 tick=10.88 ms / α=79(τ=35.3 ms) / 进入档1=60(0.0916%Δf/f)', 14, '#555')
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note(1170, 20, 470, 62, '三段横轴（跨 6 个数量级，非线性）',
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['① 微观 0~11.2 ms（一个窗） ② 中观 0~0.6 s（检测到输出）', '③ 宏观 0~12 s（基线块平均 / 上电学习）'])
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def axis(y, t0, t1, label, ticks, sub=None):
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line(X0, y, X1, y, '#888', 1.2)
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for t, s in ticks:
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x = X0 + (t - t0) / (t1 - t0) * (X1 - X0)
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line(x, y, x, y + 7, '#888', 1.0)
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txt(x, y + 23, s, 13, '#666', anchor='middle')
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txt(X0 - 14, y + 5, label, 15, '#0b2c4d', anchor='end', weight='bold')
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if sub:
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txt(X0 - 14, y + 24, sub, 12.5, '#777', anchor='end')
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return lambda t: X0 + (t - t0) / (t1 - t0) * (X1 - X0)
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# ---------------- 面板 A：一个窗（微观） ----------------
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ya, TA = 108.0, 11.2e-3
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txt(28, ya + 12, '① 微观：一个测量窗（0 ~ 11.2 ms）', 17, '#0a58ca', weight='bold')
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mx = axis(ya + 520, 0, TA, '时间', [(0, '0'), (2e-3, '2 ms'), (4e-3, '4 ms'), (6e-3, '6 ms'), (8e-3, '8 ms'), (10e-3, '10 ms'), (TA, '11.2 ms')])
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# A1 线圈频率（车辆 ⇒ 频率升高）
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yb = ya + 60
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poly([(X0, yb + 26), (mx(T_ENTER_TRUE), yb + 26), (mx(T_ENTER_TRUE), yb + 14), (X1, yb + 14)], '#c1121f', 2.0)
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line(X0 - 14, yb + 26, X0 + 4, yb + 26, '#c1121f', 1.0, dash='3,3')
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txt(X1 + 6, yb + 20, '线圈谐振频率', 13.5, '#c1121f')
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txt(X0 - 14, yb, 'A1 线圈 f', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, yb + 20, '100.000 kHz', 12.5, '#777', anchor='end')
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txt(mx(T_ENTER_TRUE) + 10, yb - 4, '车辆进入（0.6 ms）：L↓ ⇒ f↑ +0.5% ⇒ 周期↓、Xn↓', 14, '#c1121f')
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# A2 P23 方波 + 捕获点
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yc = ya + 150
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t_in0, t_in1 = XN0 / F_CAP * EDGE / EDGE, XN0 / F_CAP # 半周期 = 160µs
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pts, t, lvl = [(X0, yc + 26)], 0.0, 26
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H2 = 26
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while t < TA:
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t2 = t + t_in1
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xa2, xb2 = mx(t), mx(min(t2, TA))
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pts += [(xa2, yc + lvl), (xb2, yc + lvl)]
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lvl = 0 if lvl else 26
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t = t2
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poly(pts, '#0a7d4b', 1.6)
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ct = 0.0
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while ct <= TA:
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dot(mx(ct), yc + (26 if int(round(ct / t_in1)) % 2 == 0 else 0), 2.6, '#c1121f')
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ct += t_in1
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txt(X0 - 14, yc + 6, 'A2 P23 方波', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, yc + 26, '÷32 / 320 µs 周期', 12.5, '#777', anchor='end')
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txt(X1 + 6, yc + 14, '绿=P23，红点=捕获点（双沿）', 13.5, '#0a7d4b')
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line(mx(0.8e-3), yc + 30, mx(2.4e-3), yc + 30, '#c1121f', 1.2)
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line(mx(0.8e-3), yc + 26, mx(0.8e-3), yc + 34, '#c1121f', 1.2)
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line(mx(2.4e-3), yc + 26, mx(2.4e-3), yc + 34, '#c1121f', 1.2)
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txt(mx(1.6e-3), yc + 50, 'Xn = 相邻捕获差 = 480 tick = 160 µs（= Xn/f_cap）', 13.5, '#c1121f', anchor='middle')
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# A3 CapSum 累加台阶
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yd0, yd1 = ya + 380, ya + 240
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txt(X0 - 14, yd0, 'A3 CapSum', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, yd0 + 18, 'Σ Xn（tick）', 12.5, '#777', anchor='end')
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line(X0, yd0, X1, yd0, '#aaa', 1.0, dash='4,4')
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line(X0, yd1, X1, yd1, '#aaa', 1.0, dash='4,4')
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txt(X1 + 6, yd1 + 4, '32768（BASE 标称）', 12.5, '#777')
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txt(X1 + 6, yd0 + 4, '0', 12.5, '#777')
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n_show = min(LPCNT, 68)
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w_tot = X1 - X0
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step_w = w_tot / n_show
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acc, pts = 0, [(X0, yd0)]
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for i in range(n_show):
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acc += WIN_TICKS / LPCNT
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yv = yd0 - (acc / BASE) * (yd0 - yd1)
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pts += [(X0 + i * step_w, yv), (X0 + (i + 1) * step_w, yv)]
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poly(pts, '#e07b00', 1.8)
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line(X0 + w_tot * 0.02, yd0 + 30, X0 + w_tot * 0.96, yd0 + 30, '#e07b00', 1.2)
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txt(X0 + 8, yd1 - 14, '每 160 µs 累加一次 Xn（共 68 级台阶）⇒ CapCnt≥LPCNT 时「窗结账」', 14, '#a35b00')
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dot(X1, yd1, 4.5, '#c1121f')
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txt(X0 + 8, ya + 506, '窗结账：Value = Σ(68 个 Xn) = 32640 ⇒ CAP_OK=1 ⇒ 主循环随即调用 VD1_TASK()', 14, '#c1121f')
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# A4 Value 台阶
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ye = ya + 440
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poly([(X0, ye), (X1 * 0 + mx(10.88e-3), ye), (mx(10.88e-3), ye + 34), (X1, ye + 34)], '#0a58ca', 2.0)
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line(X0, ye, X1, ye, '#aaa', 1.0, dash='4,4')
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txt(X0 - 14, ye, 'A4 Value', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, ye + 20, '每窗更新一次', 12.5, '#777', anchor='end')
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txt(X1 + 6, ye + 4, 'Origin 基准 32640', 12.5, '#0a58ca')
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txt(X1 + 6, ye + 38, '车后 32486（−0.5%）', 12.5, '#0a58ca')
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txt(mx(10.88e-3) - 12, ye - 12, '窗边界 10.88 ms（本窗只含 0.6~10.88 ms ⇒ 部分反映）', 14, '#0a58ca', anchor='end')
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txt(X0 + 640, yd1 - 46, '窗 = LPCNT·Xn / f_cap = ⌊32768/Xn₀⌋·Xn / f_cap = 68 × 480 / 3 MHz = 10.880 ms（标称上限）', 14, '#0b2c4d', weight='bold')
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# ---------------- 面板 B：中观（检测与输出） ----------------
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ybz = ya + 560
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txt(28, ybz + 14, '② 中观：检测延迟与输出（0 ~ 600 ms） 车辆进入 @0.6 ms', 17, '#0a58ca', weight='bold')
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TB = 0.6
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mb = axis(ybz + 430, 0, TB, '时间', [(0, '0'), (0.1, '100 ms'), (0.2, '200 ms'), (0.3, '300 ms'), (0.4, '400 ms'), (0.5, '500 ms'), (TB, '600 ms')])
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ycap0, ycap1 = ybz + 330, ybz + 130 # CAPVD 纵轴：32660 → 32450
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def ycap(v): return ycap0 - (v - 32450) / (32660 - 32450) * (ycap0 - ycap1)
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txt(X0 - 14, ycap0, 'B1 CAPVD', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, ycap0 + 18, 'IIR 平滑值', 12.5, '#777', anchor='end')
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line(X0, ycap(ORIGIN), X1, ycap(ORIGIN), '#0a58ca', 1.2, dash='6,4')
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txt(X1 + 6, ycap(ORIGIN) + 4, 'Origin 32640（基线）', 12.5, '#0a58ca')
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line(X0, ycap(THR), X1, ycap(THR), '#c1121f', 1.4, dash='6,4')
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txt(X1 + 6, ycap(THR) + 4, '判据线 32611（−dlt=29 / 0.0916%）', 12.5, '#c1121f')
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def stair(seq, col, wd=1.9):
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pts, prev = [], None
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for i, (t, c) in enumerate(seq):
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x = mb(t)
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if prev is not None:
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pts += [(x, prev), (x, c)]
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else:
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pts += [(X0, c)] if False else [(x, c)]
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prev = c
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poly(pts, col, wd)
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stair([(0.0, ORIGIN)] + [(t, c) for t, _, c in big], '#c1121f', 2.0)
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stair([(0.0, ORIGIN)] + [(t, c) for t, _, c in sml], '#7aa7d9', 1.6)
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txt(mx(0) + 0, 0, '', 1)
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if T_CROSS:
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xc = mb(T_CROSS)
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dot(xc, ycap(THR), 5.0, '#c1121f')
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txt(xc + 10, ycap(THR) - 12, '越线 ⇒ 有车（大信号 Δf/f=+0.5%%：第 1 窗即越线，t≈%.1f ms）' % (T_CROSS * 1e3), 14, '#c1121f')
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if T_CROSS_S:
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xs = mb(T_CROSS_S)
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dot(xs, ycap(THR), 4.4, '#1f6fb2')
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txt(xs + 12, ycap(THR) + 46, '小信号 Δf/f=+0.1%%：≈2.2τ 才越线（t≈%.0f ms）—— τ 主导' % (T_CROSS_S * 1e3), 14, '#1f6fb2')
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note(1020, ybz + 30, 610, 110, '两种检测路径（同一条判据）', [
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'大信号（+0.5%）：第 1 窗越线，窗内平均主导',
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'小信号（+0.1%≈阈值）：≈2.2τ≈78 ms，IIR 主导',
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'⇒ 延迟 ≈ 窗平均(≤1 窗) + IIR + 输出节拍(50 ms)'])
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yt = ybz + 370
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txt(X0 - 14, yt + 8, 'B2 输出', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, yt + 26, 'FLAG_IN / 脉冲', 12.5, '#777', anchor='end')
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line(X0, yt + 34, X1, yt + 34, '#888', 1.0)
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tp = 0.0
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pts = [(X0, yt + 34)]
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while tp < TB:
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x1_, x2_ = mb(tp), mb(min(tp + 0.05, TB))
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pts += [(x1_, yt + 8), (x2_, yt + 8)]
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tp += 0.05
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x3_ = mb(min(tp, TB))
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pts += [(x2_, yt + 8), (x2_, yt + 34)] if x2_ != x3_ else []
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tp += 0.05
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poly(pts, '#666', 1.2)
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line(X0, yt + 8, mb(T_CROSS or 0.05), yt + 8, '#666', 1.6)
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x_out = mb((T_CROSS or 0.05) + 0.05)
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poly([(mb(T_CROSS or 0.05), yt + 34), (mb(T_CROSS or 0.05), yt + 6), (mb((T_CROSS or 0.05) + 0.5), yt + 6), (mb((T_CROSS or 0.05) + 0.5), yt + 34)], '#0a7d4b', 2.2)
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txt(mb(0.0) + 6, yt + 62, '50 ms 主节拍（T1 ISR）量化：FLAG_IN 在下一拍被消费 ⇒ 输出起点 ≤ 越线+50 ms。绿=继电器脉冲输出（500 ms）', 14, '#0a7d4b')
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# ---------------- 面板 C：宏观（基线） ----------------
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yc2 = ybz + 500
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txt(28, yc2 + 14, '③ 宏观：基线 Origin 的块平均（0 ~ 12 s）', 17, '#0a58ca', weight='bold')
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TC = 12.0
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mc = axis(yc2 + 180, 0, TC, '时间', [(0, '0'), (2, '2 s'), (4, '4 s'), (6, '6 s'), (8, '8 s'), (10, '10 s'), (12, '12 s')])
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yB0, yB1 = yc2 + 150, yc2 + 60
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def yb_(v): return yB0 - (v - 32620) / (32700 - 32620) * (yB0 - yB1)
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txt(X0 - 14, yB0, 'C1 Origin', 15, '#0b2c4d', anchor='end', weight='bold')
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txt(X0 - 14, yB0 + 18, '基线（块平均）', 12.5, '#777', anchor='end')
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||
seqT, x_ = [], 0.0
|
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vals = [32644, 32638, 32641, 32640, 32643, 32639, 32640, 32642, 32640, 32641, 32640, 32640]
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while x_ < TC:
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||
seqT.append((x_, vals[min(int(x_ / 1.088), len(vals) - 1)]))
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||
x_ += 1.088
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stair(seqT, '#0a58ca', 2.0)
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||
seqH, x_ = [], 0.0
|
||
valsH = [32650, 32636, 32641]
|
||
while x_ < TC:
|
||
seqH.append((x_, valsH[min(int(x_ / 5.571), 2)]))
|
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x_ += 5.571
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||
stair(seqH, '#999', 1.8)
|
||
line(X0, yb_(32640), X1, yb_(32640), '#aaa', 1.0, dash='4,4')
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dot(X0, yb_(32644), 4.2, '#c1121f')
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txt(X0 + 8, yB0 + 22, '上电：Origin = 首窗单点（噪声最大）', 13.5, '#c1121f')
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txt(X1 + 6, yb_(32640) + 4, '真值', 12.5, '#777')
|
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txt(X1 + 6, yb_(32644) - 14, 'PD132T: 100 窗=1.088 s（蓝）', 12.5, '#0a58ca')
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txt(X1 + 6, yb_(32644) + 8, 'PD136HA: 256 窗=5.571 s（灰）', 12.5, '#888')
|
||
txt(mc(3.5), yB1 - 22, '块平均 ⇒ 基线跟踪是「阶梯式」，块内一直用上一块的均值；有车期间冻结并重新累计', 14, '#0b2c4d', anchor='middle')
|
||
|
||
# ---------------- 页脚：四条关系 ----------------
|
||
note(28, yc2 + 230, 780, 126, '四条关系（讨论快慢时用「窗」最稳）', [
|
||
'① t_Xn = 分频比/(边沿系数·f_coil)：PD132T = 16/f、PD136HA = 32/f（随站点变，不是常数）',
|
||
'② Value = Σ(LPCNT 个 Xn) 是箱式积分（计数域降采样）⇒ Δf/f = 表值/65536 精确',
|
||
'③ τ/T = 256/α ⇒ α79 = 3.24 窗、α64 = 4.0 窗（与型号无关）',
|
||
'④ Origin = 块平均（100 / 256 窗），非滑动 ⇒ 基线粒度 = 1 块'])
|
||
note(830, yc2 + 230, 770, 126, 'PD136HA 对照（同站点、同 ÷32）', [
|
||
'捕获：320 µs/次（单沿，整周期 32 个线圈周期）；Xn₀ 同为 480 tick',
|
||
'窗：68 × 320 µs = 21.760 ms（标称上限 21.845）',
|
||
'τ(α64) = 87.0 ms = 4.0 窗；Origin = 256 窗 = 5.571 s',
|
||
'⇒ 全链时间 ×2（基线 ×5.1）—— 响应更慢但积分深度 ×2'])
|
||
|
||
svg = ('<svg xmlns="http://www.w3.org/2000/svg" width="%d" height="%d" viewBox="0 0 %d %d">' % (W, H, W, H)
|
||
+ '\n'.join(P) + '</svg>\n')
|
||
open(OUT, 'w', encoding='utf-8').write(svg)
|
||
print('written %s (%d bytes)' % (OUT, len(svg)))
|
||
print('XN0=%d LPCNT=%d WIN=%d tick T_WIN=%.3f ms T_XN=%.4f ms TAU=%.2f ms DLT=%d THR=%d T_CROSS=%.3f ms'
|
||
% (XN0, LPCNT, WIN_TICKS, T_WIN * 1e3, T_XN * 1e3, TAU * 1e3, DLT, THR, (T_CROSS or 0) * 1e3))
|
||
|