From b7d932b9206a3d456e896f8da7180a4b7357cf90 Mon Sep 17 00:00:00 2001 From: hub-gif <2487812171@qq.com> Date: Mon, 13 Apr 2026 10:59:22 +0800 Subject: [PATCH] =?UTF-8?q?feat(=E6=8A=A5=E5=91=8A):=20=E7=AC=AC=E5=85=AD?= =?UTF-8?q?=E7=AB=A0=E5=A2=9E=E5=8A=A0=E4=BC=98=E6=83=A0=E6=B4=BB=E5=8A=A8?= =?UTF-8?q?=E4=B8=8E=E6=A0=87=E4=BB=B7=E5=88=B8=E5=90=8E=E4=BB=B7=E5=B7=AE?= =?UTF-8?q?=E5=88=86=E6=9E=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Made-with: Cursor --- .../jd_pc_search/jd_competitor_report.py | 220 +++++++++++++++++- 1 file changed, 218 insertions(+), 2 deletions(-) diff --git a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py index 4565e31..b3a68eb 100644 --- a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py +++ b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py @@ -281,6 +281,198 @@ def _collect_prices(rows: list[dict[str, str]]) -> list[float]: return out +_JD_LIST_PRICE_KEY = "标价(jdPrice,jdPriceText,realPrice)" +_COUPON_SHOW_PRICE_KEY = ( + "券后到手价(couponPrice,subsidyPrice,finalPrice.estimatedPrice,priceShow)" +) +_ORIGINAL_LIST_PRICE_KEY = "原价(oriPrice,originalPrice,marketPrice)" +_SELLING_POINT_KEY = "卖点(sellingPoint)" +_RANK_TAGLINE_KEY = "榜单类文案(标签/腰带/标题数组中的榜、TOP 等)" + +# 列表/合并表中「卖点、腰带」常见活动话术子串（行级命中，非严谨 NLP） +_PROMO_SUBSTRINGS_IN_COPY: tuple[str, ...] = ( + "满减", + "秒杀", + "限时", + "优惠券", + "领券", + "券后", + "百亿补贴", + "包邮", + "赠品", + "买赠", + "第二件", + "第2件", + "直降", + "特价", + "促销", + "套装", + "任选", + "到手价", + "补贴", + "聚划算", + "预售", + "定金", + "返现", + "折扣", + "加购", + "下单立减", +) + + +def _analyze_price_promotions(rows: list[dict[str, str]]) -> dict[str, Any]: + """ + 从列表或合并行中归纳「标价 vs 券后/到手」及卖点/腰带中的活动话术信号， + 供 §6.1 与结构化摘要使用（**页面展示口径**，非结算实付）。 + """ + n = len(rows) + with_jd = with_cp = with_both = 0 + coupon_below = 0 + pct_offs: list[float] = [] + ori_above_list = 0 + for row in rows: + jd = _float_price(_cell(row, _JD_LIST_PRICE_KEY)) + cp = _float_price(_cell(row, _COUPON_SHOW_PRICE_KEY)) + ori = _float_price(_cell(row, _ORIGINAL_LIST_PRICE_KEY)) + if jd is not None and jd > 0: + with_jd += 1 + if cp is not None and cp > 0: + with_cp += 1 + if jd is not None and cp is not None and jd > 0 and cp > 0: + with_both += 1 + if cp + 1e-6 < jd: + coupon_below += 1 + pct_offs.append((jd - cp) / jd * 100.0) + if ( + ori is not None + and jd is not None + and ori > 0 + and jd > 0 + and ori > jd + 1e-6 + ): + ori_above_list += 1 + + selling_nonempty = sum( + 1 for r in rows if _cell(r, _SELLING_POINT_KEY).strip() + ) + rank_nonempty = sum(1 for r in rows if _cell(r, _RANK_TAGLINE_KEY).strip()) + + kw_row_hits: dict[str, int] = {} + for kw in _PROMO_SUBSTRINGS_IN_COPY: + c = 0 + for row in rows: + blob = ( + _cell(row, _SELLING_POINT_KEY) + " " + _cell(row, _RANK_TAGLINE_KEY) + ) + if kw in blob: + c += 1 + if c: + kw_row_hits[kw] = c + top_promos = sorted(kw_row_hits.items(), key=lambda x: -x[1])[:14] + + median_pct = statistics.median(pct_offs) if pct_offs else None + mean_pct = statistics.mean(pct_offs) if pct_offs else None + share_below = ( + (coupon_below / with_both) if with_both else None + ) + + return { + "row_count": n, + "rows_with_list_price": with_jd, + "rows_with_coupon_price": with_cp, + "rows_with_both_list_and_coupon": with_both, + "rows_coupon_below_list_price": coupon_below, + "share_coupon_below_list_when_both": share_below, + "median_discount_pct_when_coupon_below": median_pct, + "mean_discount_pct_when_coupon_below": mean_pct, + "rows_original_price_above_list_price": ori_above_list, + "rows_selling_point_nonempty": selling_nonempty, + "rows_rank_tagline_nonempty": rank_nonempty, + "promo_keyword_row_hits_top": [ + {"keyword": k, "rows": v} for k, v in top_promos + ], + } + + +def _markdown_price_promotion_section(p: dict[str, Any]) -> list[str]: + """§6.1 优惠活动与价差信号（Markdown 行列表）。""" + lines: list[str] = [ + "### 6.1 优惠活动与价差信号（页面展示摘录）", + "", + "- **口径**：与上节价量统计**同一批行**；比较的是列表/合并表中的**展示标价**与**展示券后/到手价**（字段见表头），" + "反映页面呈现的活动与券信息，**不等于**用户结算实付或历史最低价。", + "", + ] + wb = int(p.get("rows_with_both_list_and_coupon") or 0) + if wb <= 0: + lines.append( + "- **标价与券后价可对齐比较**的有效行不足，本节仅摘录卖点/腰带中的活动话术（若有）。" + ) + lines.append("") + else: + cb = int(p.get("rows_coupon_below_list_price") or 0) + sh = p.get("share_coupon_below_list_when_both") + med = p.get("median_discount_pct_when_coupon_below") + mean = p.get("mean_discount_pct_when_coupon_below") + lines.append( + f"- **同时解析到标价与券后/到手价** 的行：**{wb}**；其中展示「到手/券后」**严格低于**「标价」的行：**{cb}**" + + ( + f"（占可对齐行的 **{100.0 * float(sh):.1f}%**）" + if isinstance(sh, (int, float)) + else "" + ) + + "。" + ) + if med is not None: + frag_mean = ( + f"，平均价差约 **{float(mean):.1f}%**" if mean is not None else "" + ) + lines.append( + f"- **价差力度（仅「券后低于标价」子集）**：展示价差的中位数约 **{float(med):.1f}%**（相对标价）{frag_mean}；" + "通常对应满减、券、限时价等在列表上的叠加呈现。" + ) + elif cb > 0: + lines.append( + "- **价差**：存在「券后低于标价」样本，但条数较少，未给出稳健分位数；建议结合 §5 单品对照。" + ) + lines.append("") + oa = int(p.get("rows_original_price_above_list_price") or 0) + if oa > 0: + lines.append( + f"- **划线原价高于当前标价** 的行约 **{oa}** 条（常见「划线价 + 当前价」促销陈列，具体以页面为准）。" + ) + lines.append("") + sn = int(p.get("rows_selling_point_nonempty") or 0) + rn = int(p.get("rows_rank_tagline_nonempty") or 0) + nr = int(p.get("row_count") or 0) + if nr > 0: + lines.append( + f"- **卖点字段非空**：**{sn}** / **{nr}** 行；**榜单/腰带类文案非空**：**{rn}** / **{nr}** 行（用于观察申报活动与心智标签）。" + ) + lines.append("") + top = p.get("promo_keyword_row_hits_top") or [] + if isinstance(top, list) and top: + lines.append("- **活动话术在列表行中的出现面**（卖点+腰带合并扫描预设子串；**同一行可含多词**，为行级命中次数）：") + parts = [] + for it in top[:10]: + if isinstance(it, dict): + k = it.get("keyword") or "" + v = it.get("rows") + if k and v is not None: + parts.append(f"「{k}」**{int(v)}** 行") + if parts: + lines.append(" - " + "；".join(parts) + "。") + lines.append( + " - **解读**：高频词反映列表侧**主推活动类型**（如满减、百亿补贴、赠品）；与 §6 分布表结合看「低价来自常态价还是大促价带」。" + ) + else: + lines.append( + "- **活动话术**：未在卖点/腰带字段中命中预设促销子串（可能字段为空或话术与词表不一致）。" + ) + lines.append("") + return lines + + def _comment_keyword_hits( rows: list[dict[str, str]], focus_words: tuple[str, ...], @@ -1275,6 +1467,12 @@ def build_competitor_markdown( if list_export and pst_list.get("n", 0) > 0 else f"已深入抓取的 **{n_sku}** 个 SKU 合并数据中的展示价" ) + promo_rows = ( + search_export_rows + if list_export and pst_list.get("n", 0) > 0 + else merged_rows + ) + promo_sig = _analyze_price_promotions(promo_rows) hits = _comment_keyword_hits(comment_rows, focus_words) if not hits: @@ -1341,7 +1539,7 @@ def build_competitor_markdown( "", "### 1.3 方法说明（指标含义）", "", - "- **价格**：自页面「标价 / 券后价 / 详情价」等抽取的**展示价**，含促销与规格差异，**不等于**出厂价或成本。**第六章** 在具备可用的搜索列表导出时，优先以**列表全量**统计；否则使用**已深入 SKU** 的合并数据。", + "- **价格**：自页面「标价 / 券后价 / 详情价」等抽取的**展示价**，含促销与规格差异，**不等于**出厂价或成本。**第六章** 在具备可用的搜索列表导出时，优先以**列表全量**统计；否则使用**已深入 SKU** 的合并数据；**§6.1** 归纳标价与券后价差及卖点/腰带中的**活动话术信号**。", "- **品牌/店铺集中度（第四章）**：有列表全量时按列表行计店铺与品牌占比；无列表导出时按深入 SKU 合并表估算。", "- **评价主题词**：对评价正文做**预设词表子串计数**，非分词主题模型，适合扫方向，**需抽样人工验证**。", "- **用途/场景**：对每条评价独立判断是否命中预设场景词；一条可计入多个场景，统计的是「提及该场景的评价条数」而非用户数。", @@ -1420,6 +1618,13 @@ def build_competitor_markdown( f"展示价格{price_src_short}：可解析价格 **{pst['n']}** 个观测，区间约 **{pst['min']:.2f}～{pst['max']:.2f}** 元，" f"中位数 **{pst.get('median', pst['mean']):.2f}** 元。" ) + wb = int(promo_sig.get("rows_with_both_list_and_coupon") or 0) + sh = promo_sig.get("share_coupon_below_list_when_both") + med = promo_sig.get("median_discount_pct_when_coupon_below") + if wb >= 3 and isinstance(sh, (int, float)) and sh >= 0.08 and med is not None: + exec_bullets.append( + f"列表侧约 **{100.0 * float(sh):.0f}%** 可对齐行呈现「券后/到手」**低于**「标价」，展示价差中位数约 **{float(med):.1f}%**（**§6.1** 活动与话术摘录）。" + ) if multi_feedback_cat and (hits or scen_n_texts > 0): exec_bullets.append( "评价侧写（关注词、用途/场景）已按 **§5 同款细类** 分节，见 **§8.3～8.4**。" @@ -1736,8 +1941,12 @@ def build_competitor_markdown( lines.append( "**解读提示**：价差大通常反映规格、组合装、品牌溢价或促销差异；B 端定价策略需结合成本与渠道单独建模。" ) + lines.append("") + lines.extend(_markdown_price_promotion_section(promo_sig)) else: lines.append("*当前样本无可用数值价格，本节不展开统计表。*") + lines.append("") + lines.extend(_markdown_price_promotion_section(promo_sig)) lines.append("") attrs: list[str] = [] @@ -2007,6 +2216,12 @@ def build_competitor_brief( if list_export and pst_list.get("n", 0) > 0 else "keyword_pipeline_merged" ) + promo_rows_brief = ( + search_export_rows + if list_export and pst_list.get("n", 0) > 0 + else merged_rows + ) + price_promotion_signals = _analyze_price_promotions(promo_rows_brief) hits = _comment_keyword_hits(comment_rows, focus_words) if not hits: @@ -2206,6 +2421,7 @@ def build_competitor_brief( "price_stats_source": price_stats_source, "price_stats_merged_sample": pst_merged, "price_stats_list_export": pst_list if list_export else {}, + "price_promotion_signals": price_promotion_signals, "comment_focus_keywords": [ {"word": w, "count": n} for w, n in hits.most_common(24) ], @@ -2227,7 +2443,7 @@ def build_competitor_brief( "comment_sentiment_lexicon": comment_sentiment_lexicon, "notes": [ "与在线分析报告各章统计口径一致；主题词与场景为预设词表，非 NLP 主题模型。", - "价格来自页面展示字段抽取，含促销与规格差异。", + "价格来自页面展示字段抽取，含促销与规格差异；price_promotion_signals 为标价/券后对齐与卖点话术的启发式摘录。", "comment_sentiment_lexicon 为关键词粗判，非深度学习情感模型。", ], }