v2.1: DESTACO 风格选型界面 + 参数组合补全(缺组合也能出零件号)
网页版选型改版 (仅网页版, 桌面版待同步)
- 新增零件号实时条: 边选边拼, 已确定片段实心高亮, 未定片段虚化, 进度条随选推进;
浅色配色 (初版深色块与页面不协调, 已改); 选满且唯一命中时整块转青色实心
- 技术方案: 40 系列中仅 KC 有编码模板 → 改用「当前候选型号的最长公共前缀」,
已验证 40/40 系列前缀单调增长(不回退)
- 参数选择由下拉菜单改为可视化选项块: 悬停抬升+青色投影, 选中青色填充+✓,
不可选取值置灰(虚线+划线, 不消失, 让用户看到选项存在但当前组合不适用),
再点一次取消; 有说明的显示代号+说明, 纯数值用紧凑文字块
- 参数联动仍复用 allowedOptions 规则引擎, 语义未变, 只改渲染
参数组合补全
- 原逻辑: 必须有真实数模才能生成型号 → 库里没有的组合选不出来也出不了号
- infer-sku-template.py 从已有型号反推编码模板, 得 23 个系列 100% 可复现
- verify-templates.py 作为门禁: 用模板重渲染每个已有型号必须逐字一致 ——
该检查抓出 ULM/ULP 行程补零位数写错(2位写3位), 否则会生成 3000 个不存在的零件号
- gen-missing-combos.py 补全 8 个系列共 3157 个型号 (全库 54136 → 57293)
- 占位数模: 3157 个型号共用 1 个 PLACEHOLDER.step(4.5KB), 构建器按路径打包只存一份,
数据包仅 2.10GB → 2.14GB (中途用硬链接方案踩坑: NTFS 上限 1024, 超出退化为拷贝白占 500MB)
- _pending_models.csv 输出待补数模清单, 供 AI 补完后替换 step_file
新增脚本: infer-sku-template / verify-templates / quantify-combo-gap /
gen-missing-combos / gen-placeholder-step / check-combo-unique
新增文档: docs/更改说明-v2.1.md
发现未处理 (已记录在更改说明):
- 夹紧缸 9 系列一组参数对应多型号 (310 个型号, 编码含参数外尾缀, 旧码/新码混用)
- UAGP/LAGP 参数顺序与官方编码结构不符 (传感器应在转角之前)
- 17 个系列因模板不干净未补全 (宁可少补, 不生成可能不存在的零件号)
Co-Authored-By: Claude Code <noreply@anthropic.com>
This commit is contained in:
101
onebot-data/tools/quantify-combo-gap.py
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101
onebot-data/tools/quantify-combo-gap.py
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# -*- coding: utf-8 -*-
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"""
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量化"参数组合缺口": 合法组合 (域 + 规则内) vs 库中已有变体, 以及补全的规模。
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对每个系列:
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- 现有变体数
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- 笛卡尔积规模 (各参数值域相乘)
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- 规则过滤后的合法组合数 (复用 C# Configurator 同语义的规则求值)
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- 缺口 = 合法组合 - 已覆盖
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这决定"补全组合数据"的可行性与数据量。
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"""
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import json, io, itertools
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CAT = r'D:/开发/OnebotCatalog/release/web_2026.08/catalog.json'
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d = json.load(io.open(CAT, encoding='utf-8-sig'))
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def S(v):
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return '' if v is None else str(v)
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def eq(a, b):
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return S(a).lower() == S(b).lower()
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def cond_match(c, params):
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"""规则条件求值 (与 app.js condMatches / C# CondMatches 同语义)。"""
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if not c:
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return True
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val = params.get(c['Param'])
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if val is None:
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return False
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op = c['Op']
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cv = c['Value']
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if op == 'eq':
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return eq(val, cv)
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if op == 'ne':
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return not eq(val, cv)
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if op == 'in':
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return isinstance(cv, list) and any(eq(val, x) for x in cv)
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try:
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a, b = float(S(val)), float(S(cv))
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except ValueError:
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return False
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return {'gt': a > b, 'ge': a >= b, 'lt': a < b, 'le': a <= b}.get(op, False)
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def rule_violated(rules, params):
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for r in rules or []:
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if cond_match(r.get('If'), params) and not cond_match(r.get('Then'), params):
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return True
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return False
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print(f'{"系列":7s} {"现有":>6s} {"笛卡尔":>9s} {"规则内":>9s} {"缺口":>9s} {"倍数":>5s}')
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print('-' * 62)
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tot_exist = tot_gap = 0
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rows = []
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for s in d['series']:
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vs = s.get('variants') or []
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if not vs:
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continue
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params = s['parameters']
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doms = []
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for p in params:
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vals = p.get('values') or []
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if not vals:
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doms = None
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break
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doms.append(vals)
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if doms is None:
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rows.append((s['code'], len(vs), None, None, None))
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continue
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cart = 1
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for vals in doms:
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cart *= len(vals)
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# 规则过滤 (笛卡尔过大时抽样估算)
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legal = 0
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if cart <= 400000:
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for combo in itertools.product(*doms):
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prm = {p['code']: v for p, v in zip(params, combo)}
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if not rule_violated(s.get('rules'), prm):
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legal += 1
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else:
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legal = None # 太大, 跳过精确统计
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gap = (legal - len(vs)) if legal is not None else None
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rows.append((s['code'], len(vs), cart, legal, gap))
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if legal is not None:
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tot_exist += len(vs)
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tot_gap += max(0, gap)
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for code, n, cart, legal, gap in sorted(rows, key=lambda x: -(x[4] or -1)):
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c = f'{cart:,}' if cart else '—'
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l = f'{legal:,}' if legal else '—'
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g = f'{gap:,}' if gap is not None else '—'
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mult = f'{legal/n:.1f}x' if legal and n else '—'
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flag = ' <-- 缺口大' if gap and gap > 50000 else ''
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print(f'{code:7s} {n:6d} {c:>9s} {l:>9s} {g:>9s} {mult:>5s}{flag}')
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print(f'\n合计: 现有 {tot_exist:,} 变体, 缺口 {tot_gap:,} 组合')
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print(f'补全后约 {tot_exist + tot_gap:,} 变体 (是现在的 {(tot_exist+tot_gap)/tot_exist:.1f} 倍)')
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