# -*- coding: utf-8 -*- """生成 v2 看板：对照《经营管理指标日常播报》大区表的结构与口径""" import json, datetime P = json.load(open('南部大区城市看板/daqu_parsed.json')) D = json.load(open('南部大区城市看板/dashboard_data.json')) E = json.load(open('南部大区城市看板/extra_data.json')) meta, region, award = P['meta'], P['region'], P['award'] # 经营奖起算线按季度目标 100%（2026-08-19 用户口径修正，原播报表为 80%） award['start_line'] = award['q_targets']['合计'] award['achieve_progress'] = award['achieve_gp']['合计'] / award['start_line'] # 时间进度按日历自算（季度首日到 as_of 的天数/季度总天数），不信源字段（源 6/7 口径错误，2026-08-19 修正） _as_of = datetime.date.fromisoformat(meta['as_of']) _q_start = datetime.date(_as_of.year, (_as_of.month - 1) // 3 * 3 + 1, 1) _q_end_m = _q_start.month + 2 _q_end = datetime.date(_as_of.year, _q_end_m, [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31][_q_end_m - 1]) award['time_progress'] = ((_as_of - _q_start).days + 1) / ((_q_end - _q_start).days + 1) award['gap_vs_time'] = award['achieve_gp']['合计'] - award['start_line'] * award['time_progress'] # 剩余天数/需日均/季末预测（2026-08-19 修正：源 rest_daily 按 13 天除，错误） award['elapsed_days'] = (_as_of - _q_start).days + 1 award['total_days'] = (_q_end - _q_start).days + 1 award['remaining_days'] = award['total_days'] - award['elapsed_days'] award['rest_daily'] = -(abs(award['target_gap']) / award['remaining_days']) award['current_level'] = region['毛利额']['t1'] award['projected_total'] = award['achieve_gp']['合计'] + award['current_level'] * award['remaining_days'] award['projected_gap'] = award['projected_total'] - award['start_line'] cities12 = P['cities'] region_daily = P['region_daily'] city_daily = P['city_daily'] DIRECT = list(cities12.keys()) LIANYING = [r['city'] for r in D['city_rows'] if r['type'] != '直营'] trend, city_rows = D['trend'], {r['city']: r for r in D['city_rows']} # 最近14天大区日序列 last14 = region_daily[-14:] payload = { 'meta': meta, 'region': region, 'award': award, 'cities12': cities12, 'region_daily14': last14, 'city_daily14': {c: rows[-14:] for c, rows in city_daily.items()}, 'city_ext': P['city_ext'], 'contrib': P['contrib'], 'risk': P['risk'], 'lianying': [], 'months': D['months'], 'mlabel': D['mlabel'], 'agg_trend': D['agg_trend'], 'city_trend': trend, 'agg_cards': D['cards'], 'gp_user_core': E['gp_user_core'], 'ly_modes': E['ly_modes'], 'targets': E['targets'], } for c in LIANYING: jul = city_rows.get(c, {}) aug = trend.get(c, {}).get('202608', {}) payload['lianying'].append({ 'city': c, 'mode': E['ly_modes'].get(c, '-'), 'target': E['targets']['lianying'].get(c), 'aug_rev': aug.get('rev'), 'aug_gpm': aug.get('gpm'), 'aug_users': aug.get('users'), 'aug_kedanjia': aug.get('kedanjia'), 'jul_rev': jul.get('rev'), 'jul_gp': jul.get('gp'), 'jul_gpm': jul.get('gpm'), 'jul_users': jul.get('users'), 'jul_rev_mom': jul.get('rev_mom'), 'jul_gp_user': jul.get('gp_user'), 'jul_kedanjia': jul.get('kedanjia'), }) payload['lianying'].sort(key=lambda x: -(x['aug_rev'] or 0)) # ---------- 不达标城市归因 ---------- tp_v = meta['time_progress'] _agg = {} for _k in ['收入', '租金', '电费', '全职人力&资产折旧', '安装成本', '其他成本', '营销费', '毛利额']: _agg[_k] = sum((v.get(_k) or {}).get('mtd') or 0 for v in cities12.values()) BENCH = {k: _agg[k] / _agg['收入'] for k in ['租金', '电费', '全职人力&资产折旧', '安装成本', '其他成本', '营销费']} BENCH['gpm'] = _agg['毛利额'] / _agg['收入'] BENCH['kd'] = region_daily[-1]['kedanjia_noka'] attribution = [] for c, v in cities12.items(): g = lambda k, f='mtd': (v.get(k) or {}).get(f) if isinstance(v.get(k), dict) else None vs = v.get('月度目标达成进度VS时间进度') if vs is None or vs >= 0: continue rev, gp = g('收入'), g('毛利额') gpm, gpm_tgt, gp_tgt = g('毛利率'), v.get('毛利率目标'), v.get('毛利额目标') imp_tgt = gp_tgt / gpm_tgt if gpm_tgt else None rev_ach = rev / imp_tgt if imp_tgt else None rev_drag = (rev_ach - tp_v) * 100 if rev_ach else 0 gpm_drag = (gpm - gpm_tgt) * 100 if gpm and gpm_tgt else 0 if rev_drag < -1 and gpm_drag < -1: verdict = '收入与毛利率双拖' elif gpm_drag < -1: verdict = '成本侧拖累为主（毛利率不达标）' else: verdict = '收入/规模拖累为主（量不够）' drags = [] for k in ['电费', '租金', '全职人力&资产折旧', '安装成本', '其他成本', '营销费']: m = g(k) if not m: continue rate = m / rev tgt, lab = (0.30, '目标') if k == '电费' else (BENCH[k], '基准') d = rate - tgt if d > 0.003: drags.append({'name': k, 'rate': rate, 'bench': tgt, 'lab': lab, 'd': d, 'impact': -d * rev}) drags.sort(key=lambda x: x['impact']) dl = city_daily.get(c, [])[-14:] u_chg = (dl[-1]['users'] - dl[0]['users']) / dl[0]['users'] if dl and dl[0]['users'] else None kd = g('客单价（去KA）', 't1') jul = city_rows.get(c, {}) attribution.append({ 'city': c, 'vs': vs, 'gp_gap': v.get('毛利额GAP'), 'rev_ach': rev_ach, 'rev_drag': rev_drag, 'imp_tgt': imp_tgt, 'rev': rev, 'gpm': gpm, 'gpm_tgt': gpm_tgt, 'gpm_drag': gpm_drag, 'verdict': verdict, 'drags': drags[:3], 'u_chg': u_chg, 'kd': kd, 'kd_bench': BENCH['kd'], 'jul_gpm': jul.get('gpm'), 'jul_gpm_mom': jul.get('gpm_mom'), }) attribution.sort(key=lambda x: x['vs']) payload['attribution'] = attribution # ---------- 城市 T-1 vs T-2 日环比（明细展开用） ---------- city_dod = {} for c, rows in city_daily.items(): if len(rows) < 2: continue a, b = rows[-1], rows[-2] if not a.get('rev') or not b.get('rev'): continue hr = lambda r: r['renli'] + r['btrydep'] + r['cabtdep'] city_dod[c] = { '租金': a['zujin'] - b['zujin'], '电费': a['dianfei'] - b['dianfei'], '电费费率': a['dianfei'] / a['rev'] - b['dianfei'] / b['rev'], '单用户用电量': (a.get('kwh_user') or 0) - (b.get('kwh_user') or 0), '全职人力&资产折旧': hr(a) - hr(b), '安装成本': a['anzhuang'] - b['anzhuang'], '其他成本': a['qita_cost'] - b['qita_cost'], '营销费': a['yingxiao'] - b['yingxiao'], '营销费率': a['yingxiao'] / a['rev'] - b['yingxiao'] / b['rev'], } payload['city_dod'] = city_dod JSON_DATA = json.dumps(payload, ensure_ascii=False) html = """ 南部大区经营管理看板 · 8月MTD播报

南部大区 · 经营管理指标看板

Q3换电毛利额目标进度 毛利额口径：7月+8月(≤8/20)为管报口径，8/21起为线上数据

近14天 收入 / 毛利额 / 毛利率

近14天 换电服务用户数 / 单用户毛利

直营12城 · 8月MTD播报 点行展开：经营趋势与预估 / 电费异动 / 成本结构 · 进度VS时间<0 的城市行下附归因 · 进度VS时间 = 毛利目标达成率 − 时间进度() · 毛利GAP = MTD毛利 − 月目标×时间进度

大区城市变化贡献排名（T-1 vs T-2 日环比） 按城市变化额排名 · 贡献占比 = 城市变化 ÷ 大区合计变化 · 覆盖南部全部城市

经营风险矩阵 X=毛利率 MTD vs 目标缺口（无目标城市按25%参考线） · Y=近7日收入周环比 · 气泡=收入MTD规模 · 左下=缺口大且走弱（重点风险）

联营城市 · 8月MTD + 7月对照 8月为MTD（截至""" + str(int(meta['mtd_end'][6:])) + """日），点列头可排序 · 模式＝联营分润模式（空值记“标准”）

大区月度走势（5–7月全月 · 8月MTD+月末预测）

8月柱＝MTD实绩（深色）＋月末预测（浅色），预测＝MTD÷已过天数×全月天数；毛利率/换电用户为MTD实绩口径。

单城市月度走势

""" open('南部大区城市看板/南部大区经营看板_v2.html', 'w', encoding='utf-8').write(html) print('written', len(html), 'chars')