340 lines
12 KiB
Python
340 lines
12 KiB
Python
"""销量计算相关函数"""
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import math
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from datetime import datetime, timedelta
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from services.db import query
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from services.cache import RefreshableCache, register_cache
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def _build_daily_sales_data(rows):
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"""从SQL查询结果构建 {product_code: {date: qty}} 数据结构"""
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daily = {}
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for r in rows:
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pc = r['货品编号']
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d = r['日期'].strftime('%Y-%m-%d') if hasattr(r['日期'], 'strftime') else str(r['日期'])[:10]
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qty = int(r['日销量']) if r['日销量'] else 0
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if pc not in daily:
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daily[pc] = {}
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daily[pc][d] = qty
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return daily
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def _calc_stddev(qty_list):
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"""计算标准差"""
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n = len(qty_list)
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if n < 2:
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return 0
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avg = sum(qty_list) / n
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variance = sum((x - avg) ** 2 for x in qty_list) / n
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return math.sqrt(variance)
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def _compute_product_sales():
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"""计算每个产品的昨日销量、多天平均/最高/最低/标准差"""
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# 获取最新日期
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latest = query("SELECT MAX(日期) AS d FROM daily_outbound")[0]['d']
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if hasattr(latest, 'strftime'):
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latest_str = latest.strftime('%Y-%m-%d')
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else:
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latest_str = str(latest)[:10]
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latest_dt = datetime.strptime(latest_str, '%Y-%m-%d')
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# 计算各时间范围（扩展到60天）
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dates_3 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(3)]
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dates_7 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(7)]
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dates_14 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(14)]
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dates_30 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(30)]
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dates_60 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(60)]
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# 查询所有产品的每日销量（60天范围）
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rows = query("""
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SELECT 货品编号, 日期, SUM(货品数量) AS 日销量
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FROM daily_outbound
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WHERE 日期 >= %s
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GROUP BY 货品编号, 日期
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""", (dates_60[-1],))
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# 构建数据结构
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daily = _build_daily_sales_data(rows)
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result = {}
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for pc, dmap in daily.items():
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yesterday_qty = dmap.get(latest_str, 0)
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# 各范围数值
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qty_3 = [dmap.get(d, 0) for d in dates_3]
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qty_7 = [dmap.get(d, 0) for d in dates_7]
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qty_14 = [dmap.get(d, 0) for d in dates_14]
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qty_30 = [dmap.get(d, 0) for d in dates_30]
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qty_60 = [dmap.get(d, 0) for d in dates_60]
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# 平均值
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avg_3 = int(round(sum(qty_3) / 3))
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avg_7 = int(round(sum(qty_7) / 7))
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avg_14 = int(round(sum(qty_14) / 14))
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avg_30 = int(round(sum(qty_30) / 30))
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avg_60 = int(round(sum(qty_60) / 60))
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# 最高值
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max_3 = max(qty_3)
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max_7 = max(qty_7)
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max_14 = max(qty_14)
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max_30 = max(qty_30)
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# 最低值
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min_14 = min(qty_14)
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# 14天标准差
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std_14 = int(round(_calc_stddev(qty_14)))
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# 偏差率计算（防止除零）
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base = avg_3 if avg_3 != 0 else 1
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最低销量偏差率 = round((min_14 - avg_3) / base, 4)
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最高销量偏差率 = round((max_14 - avg_3) / base, 4)
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# 极值判断
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极小值 = 0.7
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极大值 = 1
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if 最低销量偏差率 <= 极小值 and 最高销量偏差率 >= 极大值:
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极值判断 = '极大/极小'
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elif 最低销量偏差率 < 极小值:
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极值判断 = '极小'
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elif 最高销量偏差率 > 极大值:
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极值判断 = '极大'
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else:
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极值判断 = '正常'
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# 14天波动率与波动风险判断
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avg_base = avg_14 if avg_14 != 0 else 1
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波动率 = round(std_14 / avg_base, 4)
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if 波动率 <= 0.5:
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波动风险 = 'X'
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elif 波动率 <= 1:
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波动风险 = 'Y'
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else:
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波动风险 = 'Z'
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result[pc] = {
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'昨日销量': yesterday_qty,
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'3天平均销量': avg_3,
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'7天平均销量': avg_7,
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'14天平均销量': avg_14,
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'30天平均销量': avg_30,
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'60天平均销量': avg_60,
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'3天最高销量': max_3,
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'7天最高销量': max_7,
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'14天最高销量': max_14,
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'30天最高销量': max_30,
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'14天最低销量': min_14,
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'14天标准差': std_14,
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'最低销量偏差率': 最低销量偏差率,
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'最高销量偏差率': 最高销量偏差率,
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'极值判断': 极值判断,
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'14天波动率': 波动率,
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'波动风险': 波动风险,
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}
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return result
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def _get_warehouse_names_by_property(property_keyword):
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"""获取指定仓库属性标签下的所有仓库名称列表"""
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rows = query(
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"SELECT warehouse_name FROM warehouses WHERE FIND_IN_SET(%s, warehouse_property) > 0",
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(property_keyword,)
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)
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return [r['warehouse_name'] for r in rows if r['warehouse_name']]
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def _compute_product_sales_cloud_yesterday():
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"""计算每个产品的云仓昨日销量（仅WDT来源，仓库属性含云仓）"""
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latest = query("SELECT MAX(日期) AS d FROM daily_outbound")[0]['d']
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if hasattr(latest, 'strftime'):
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latest_str = latest.strftime('%Y-%m-%d')
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else:
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latest_str = str(latest)[:10]
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cloud_wh_names = _get_warehouse_names_by_property('云仓')
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if not cloud_wh_names:
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return {}
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# 清洗仓库名中的Excel公式格式（如="1蝶雅广州新仓(蝶雅自营)" → 1蝶雅广州新仓(蝶雅自营)）
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rows = query(f"""
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SELECT 货品编号, SUM(货品数量) AS total_qty
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FROM daily_outbound
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WHERE 数据来源 = 'wdt'
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AND 日期 = %s
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AND REPLACE(REPLACE(仓库, '="', ''), '"', '') IN ({','.join(['%s']*len(cloud_wh_names))})
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GROUP BY 货品编号
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""", (latest_str,) + tuple(cloud_wh_names))
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result = {}
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for r in rows:
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result[r['货品编号']] = int(r['total_qty']) if r['total_qty'] else 0
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return result
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def _compute_product_sales_external_yesterday():
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"""计算每个产品的外仓昨日销量：
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- WDT来源且仓库属性含外仓
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- tmgj/jdjwt/jdgj/jdbz所有来源（全部算外仓）
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"""
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latest = query("SELECT MAX(日期) AS d FROM daily_outbound")[0]['d']
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if hasattr(latest, 'strftime'):
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latest_str = latest.strftime('%Y-%m-%d')
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else:
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latest_str = str(latest)[:10]
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ext_wh_names = _get_warehouse_names_by_property('外仓')
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ext_wh_ph = ','.join(['%s'] * len(ext_wh_names)) if ext_wh_names else ''
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# 外仓条件：平台来源全部算外仓 + WDT外仓（仓库名含Excel公式格式需清洗）
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where_parts = ["数据来源 IN ('tmgj','jdjwt','jdgj','jdbz','dymuto','dymtuo','jdsmj')"]
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wdt_params = []
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if ext_wh_names:
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clean_sql = "REPLACE(REPLACE(仓库, '=\"', ''), '\"', '')"
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where_parts.append(f"(数据来源 = 'wdt' AND {clean_sql} IN ({ext_wh_ph}))")
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wdt_params = list(ext_wh_names)
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where_sql = '(' + ' OR '.join(where_parts) + ')'
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params = [latest_str] + wdt_params
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rows = query(f"""
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SELECT 货品编号, SUM(货品数量) AS total_qty
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FROM daily_outbound
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WHERE 日期 = %s AND {where_sql}
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GROUP BY 货品编号
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""", params)
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result = {}
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for r in rows:
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result[r['货品编号']] = int(r['total_qty']) if r['total_qty'] else 0
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return result
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# 可刷新的缓存实例
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sales_cache = RefreshableCache(maxsize=1)
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register_cache(sales_cache)
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@sales_cache.wrap
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def compute_product_sales_cached():
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"""缓存版销量计算（支持主动刷新）"""
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return _compute_product_sales()
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# 云仓/外仓昨日销量缓存
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cloud_sales_cache = RefreshableCache(maxsize=1)
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register_cache(cloud_sales_cache)
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ext_sales_cache = RefreshableCache(maxsize=1)
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register_cache(ext_sales_cache)
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@cloud_sales_cache.wrap
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def compute_product_sales_cloud_yesterday_cached():
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"""缓存版云仓昨日销量"""
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return _compute_product_sales_cloud_yesterday()
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@ext_sales_cache.wrap
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def compute_product_sales_external_yesterday_cached():
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"""缓存版外仓昨日销量"""
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return _compute_product_sales_external_yesterday()
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def _get_wh_names_by_property(prop):
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"""获取指定属性的仓库名称列表（含清洗）"""
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return _get_warehouse_names_by_property(prop)
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def _compute_product_sales_extended():
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"""计算总销量 + 云仓销量 + 外仓销量的完整数据结构（含3/7/14天平均等）"""
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latest = query("SELECT MAX(日期) AS d FROM daily_outbound")[0]['d']
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if hasattr(latest, 'strftime'):
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latest_str = latest.strftime('%Y-%m-%d')
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else:
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latest_str = str(latest)[:10]
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latest_dt = datetime.strptime(latest_str, '%Y-%m-%d')
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# 云仓/外仓仓库名
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cloud_wh = _get_warehouse_names_by_property('云仓')
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ext_wh = _get_warehouse_names_by_property('外仓')
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dates_3 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(3)]
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dates_7 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(7)]
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dates_14 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(14)]
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dates_30 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(30)]
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dates_60 = [(latest_dt - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(60)]
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# 一次查询所有数据，带来源和仓库信息
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rows = query("""
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SELECT 货品编号, 日期, 数据来源, REPLACE(REPLACE(仓库, '="', ''), '"', '') AS 仓库名, 货品数量
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FROM daily_outbound
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WHERE 日期 >= %s
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""", (dates_60[-1],))
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# 构建三套数据结构
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from collections import defaultdict
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total_daily = defaultdict(lambda: defaultdict(int))
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cloud_daily = defaultdict(lambda: defaultdict(int))
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ext_daily = defaultdict(lambda: defaultdict(int))
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cloud_wh_set = set(cloud_wh)
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ext_wh_set = set(ext_wh)
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ext_sources = {'tmgj', 'jdjwt', 'jdgj', 'jdbz', 'dymuto', 'dymtuo', 'jdsmj'}
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for r in rows:
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pc = r['货品编号']
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d = r['日期'].strftime('%Y-%m-%d') if hasattr(r['日期'], 'strftime') else str(r['日期'])[:10]
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qty = int(r['货品数量']) if r['货品数量'] else 0
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src = r['数据来源']
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wh = r['仓库名'] or ''
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# 总销量
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total_daily[pc][d] += qty
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# 云仓销量：WDT + 云仓仓库
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if src == 'wdt' and wh in cloud_wh_set:
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cloud_daily[pc][d] += qty
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# 外仓销量：WDT+外仓仓库 或 4个外仓来源
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if (src == 'wdt' and wh in ext_wh_set) or src in ext_sources:
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ext_daily[pc][d] += qty
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def _compute_from_daily(daily_map):
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"""从daily map计算销量统计"""
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result = {}
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for pc, dmap in daily_map.items():
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qty_3 = [dmap.get(d, 0) for d in dates_3]
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qty_7 = [dmap.get(d, 0) for d in dates_7]
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qty_14 = [dmap.get(d, 0) for d in dates_14]
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qty_30 = [dmap.get(d, 0) for d in dates_30]
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qty_60 = [dmap.get(d, 0) for d in dates_60]
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result[pc] = {
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'昨日销量': dmap.get(latest_str, 0),
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'3天平均销量': int(round(sum(qty_3)/3)),
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'7天平均销量': int(round(sum(qty_7)/7)),
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'14天平均销量': int(round(sum(qty_14)/14)),
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'30天平均销量': int(round(sum(qty_30)/30)),
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'60天平均销量': int(round(sum(qty_60)/60)),
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'3天最高销量': max(qty_3),
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'7天最高销量': max(qty_7),
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'14天最高销量': max(qty_14),
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'30天最高销量': max(qty_30),
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}
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return result
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return {
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'total': _compute_from_daily(total_daily),
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'cloud': _compute_from_daily(cloud_daily),
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'external': _compute_from_daily(ext_daily),
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}
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# 总+云仓+外仓完整销量缓存
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extended_sales_cache = RefreshableCache(maxsize=1)
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register_cache(extended_sales_cache)
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@extended_sales_cache.wrap
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def compute_product_sales_extended_cached():
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"""缓存版总销量+云仓+外仓完整数据结构"""
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return _compute_product_sales_extended()
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