feat(jd-pipeline): matrix chart and brief use totalSales for sales volume

Made-with: Cursor

backend/crawler_copy/jd_pc_search/jd_competitor_report.py

@@ -1578,7 +1578,7 @@ def _competitor_matrix_md_line(
     )
     cat = _md_cell(_detail_category_path_cell(row), 24)
     ing = _matrix_ingredients_cell(row)
-    cc = _md_cell(_cell(row, "评价量(commentFuzzy)"), 10)
+    cc = _md_cell(_cell(row, "销量口径(totalSales)", "评价量(commentFuzzy)"), 14)
     prev = _md_cell(_cell(row, "comment_preview"), 72)
     return (
         f"| {sku} | {title} | {brand} | {pj} | {df} | {shop} | {sell} | {rank} | "
@@ -1664,10 +1664,10 @@ def _focus_scenario_combo_bar_filename(group: str, index: int) -> str:
     return f"chart_focus_and_scenarios_bar__{slug}.png"
 
 
-def _matrix_prices_reviews_chart_filename(group: str, index: int) -> str:
+def _matrix_prices_sales_chart_filename(group: str, index: int) -> str:
-    """与 ``report_charts.generate_report_charts`` 中 ``chart_matrix_prices_reviews__*`` 一致。"""
+    """与 ``report_charts.generate_report_charts`` 中 ``chart_matrix_prices_sales__*`` 一致。"""
     slug = _scenario_group_asset_slug(group, index)
-    return f"chart_matrix_prices_reviews__{slug}.png"
+    return f"chart_matrix_prices_sales__{slug}.png"
 
 
 def _lines_4_reading_brand(
@@ -2219,8 +2219,8 @@ def build_competitor_markdown(
             "分组**仅**使用合并表列 ``detail_category_path``（商详类目路径）：**三级路径**取中间一段（如 … > **饼干** > 粗粮饼干），"
             "**四级及以上**取倒数第二段（如 … > **面条** > 挂面）。**该列为空**或路径段均为内部编码、**无法解析出可读细类**的 SKU **不进入**本矩阵，亦**不参与**第八章按细类的评价统计。",
             "",
-            "**读图方式**：每个细类下为**并列横向条形图**（左：**展示价**（元）；右：**评价量**（搜索列表侧文案，作**声量 proxy**，**非**平台销量/动销）），"
+            "**读图方式**：每个细类下为**并列横向条形图**（左：**展示价**（元）；右：**销量**（搜索列表 ``totalSales`` 文案解析件数，如「已售50万+」计为 **50 万**）），"
-            "纵轴为**产品标题**（与 ``report_assets/chart_matrix_prices_reviews__*.png`` 同源）。**SKU、店铺、配料与评价摘要等明细不列入正文**，见本批次 ``keyword_pipeline_merged.csv``。",
+            "纵轴为**产品标题**（与 ``report_assets/chart_matrix_prices_sales__*.png`` 同源）。**SKU、店铺、配料与评价摘要等明细不列入正文**，见本批次 ``keyword_pipeline_merged.csv``。",
             "",
         ]
     )
@@ -2237,12 +2237,12 @@ def build_competitor_markdown(
     for gi, (gname, grows) in enumerate(grouped_matrix):
         lines.append(f"### {gname}（**{len(grows)}** 款）")
         lines.append("")
-        mx_chart = _matrix_prices_reviews_chart_filename(gname, gi)
+        mx_chart = _matrix_prices_sales_chart_filename(gname, gi)
         lines.extend(
             _embed_chart(
                 run_dir,
                 mx_chart,
-                f"「{_md_cell(gname, 20)}」· 展示价与评价量（搜索侧声量 proxy）；纵轴为产品标题。",
+                f"「{_md_cell(gname, 20)}」· 展示价与销量（totalSales 解析）；纵轴为产品标题。",
             )
         )
         if not (run_dir / "report_assets" / mx_chart).is_file():
@@ -2665,6 +2665,7 @@ def build_competitor_brief(
                     "category": _detail_category_path_cell(row),
                     "selling_point": _cell(row, "卖点(sellingPoint)")[:240],
                     "comment_fuzzy": _cell(row, "评价量(commentFuzzy)"),
+                    "total_sales": _cell(row, "销量口径(totalSales)"),
                 }
             )
         matrix_groups.append(

backend/crawler_copy/jd_pc_search/jd_keyword_pipeline.py

@@ -200,6 +200,7 @@ _MERGED_LEAN_FIELDNAMES: tuple[str, ...] = (
     "榜单类文案(标签/腰带/标题数组中的榜、TOP 等)",
     "评价量(commentFuzzy)",
     "销量楼层(commentSalesFloor)",
+    "销量口径(totalSales)",
     "店铺名(shopName)",
     "商品链接(toUrl,clickUrl,item.m.jd.com)",
     "主图(imageurl,imageUrl)",

backend/crawler_copy/jd_pc_search/search/jd_h5_search_requests.py

@@ -339,6 +339,7 @@ JD_ITEM_CSV_FIELDS = (
     "original_price",
     "selling_point",
     "comment_sales_floor",
+    "total_sales",
     "hot_list_rank",
     "comment_count",
     "shop_name",
@@ -369,6 +370,7 @@ JD_EXPORT_COLUMN_HEADERS: dict[str, str] = {
     "original_price": "原价(oriPrice,originalPrice,marketPrice)",
     "selling_point": "卖点(sellingPoint)",
     "comment_sales_floor": "销量楼层(commentSalesFloor)",
+    "total_sales": "销量口径(totalSales)",
     "hot_list_rank": "榜单类文案(标签/腰带/标题数组中的榜、TOP 等)",
     "comment_count": "评价量(commentFuzzy)",
     "shop_name": "店铺名(shopName)",
@@ -1239,6 +1241,13 @@ def _normalize_jd_api_row(d: dict[str, Any], *, keyword: str, page: int) -> dict
         120,
     )
     comment_sales_floor = _jd_format_comment_sales_floor(d)
+    total_sales = _human_text(
+        _sval_jd(
+            d,
+            ("totalSales", "total_sales", "TotalSales"),
+        ),
+        400,
+    )
 
     tag_strs = _jd_iter_tag_strings(d)
     arr_ind, _arr_groups = _jd_collect_list_string_fragments(
@@ -1301,6 +1310,7 @@ def _normalize_jd_api_row(d: dict[str, Any], *, keyword: str, page: int) -> dict
     out["original_price"] = orig
     out["selling_point"] = selling_point
     out["comment_sales_floor"] = comment_sales_floor
+    out["total_sales"] = total_sales
     out["hot_list_rank"] = hot_list_rank
     out["comment_count"] = comment_count
     out["shop_name"] = shop

backend/pipeline/csv_schema.py

@@ -14,6 +14,7 @@ JD_SEARCH_INTERNAL_KEYS: tuple[str, ...] = (
     "original_price",
     "selling_point",
     "comment_sales_floor",
+    "total_sales",
     "hot_list_rank",
     "comment_count",
     "shop_name",
@@ -39,6 +40,7 @@ JD_SEARCH_CSV_HEADERS: dict[str, str] = {
     "original_price": "原价(oriPrice,originalPrice,marketPrice)",
     "selling_point": "卖点(sellingPoint)",
     "comment_sales_floor": "销量楼层(commentSalesFloor)",
+    "total_sales": "销量口径(totalSales)",
     "hot_list_rank": "榜单类文案(标签/腰带/标题数组中的榜、TOP 等)",
     "comment_count": "评价量(commentFuzzy)",
     "shop_name": "店铺名(shopName)",
@@ -117,6 +119,7 @@ MERGED_SEARCH_CSV_COLUMNS: tuple[str, ...] = (
     "榜单类文案(标签/腰带/标题数组中的榜、TOP 等)",
     "评价量(commentFuzzy)",
     "销量楼层(commentSalesFloor)",
+    "销量口径(totalSales)",
     "店铺名(shopName)",
     "商品链接(toUrl,clickUrl,item.m.jd.com)",
     "主图(imageurl,imageUrl)",
@@ -138,6 +141,7 @@ MERGED_SEARCH_INTERNAL_KEYS: tuple[str, ...] = (
     "hot_list_rank",
     "comment_fuzzy",
     "comment_sales_floor",
+    "total_sales",
     "shop_name",
     "detail_url",
     "image",

backend/pipeline/migrations/0014_jdjobmergedrow_total_sales.py

@@ -0,0 +1,18 @@
+# Generated manually for keyword_pipeline_merged.csv column 销量口径(totalSales).
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("pipeline", "0013_rebuild_pipelinejobcheckpoint"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="jdjobmergedrow",
+            name="total_sales",
+            field=models.TextField(blank=True, default=""),
+        ),
+    ]

backend/pipeline/models.py

@@ -277,6 +277,7 @@ class JdJobMergedRow(models.Model):
     hot_list_rank = models.TextField(blank=True, default="")
     comment_fuzzy = models.TextField(blank=True, default="")
     comment_sales_floor = models.TextField(blank=True, default="")
+    total_sales = models.TextField(blank=True, default="")
     shop_name = models.TextField(blank=True, default="")
     detail_url = models.TextField(blank=True, default="")
     image = models.TextField(blank=True, default="")

backend/pipeline/report_charts.py

@@ -80,11 +80,17 @@ def _float_price_from_cell(s: str) -> float | None:
     return None
 
 
-def _reviews_volume_int(s: str) -> int:
+def _cn_volume_int(s: str) -> int:
-    """与矩阵「评价量」列同源：从搜索侧模糊文案中抽取整数（含「万」）。"""
+    """
+    从搜索侧文案抽取非负整数（评价量/销量等）：支持「亿」「万」及纯数字；
+    如 ``已售50万+ | good:99%好评`` → 500000。
+    """
     t = (s or "").strip().replace(",", "").replace("，", "")
     if not t:
         return 0
+    m = re.search(r"(\d+(?:\.\d+)?)\s*亿", t)
+    if m:
+        return int(round(float(m.group(1)) * 100_000_000))
     m = re.search(r"(\d+(?:\.\d+)?)\s*万", t)
     if m:
         return int(round(float(m.group(1)) * 10_000))
@@ -97,7 +103,7 @@ def _reviews_volume_int(s: str) -> int:
 def _format_xaxis_int_cn(x: float, _pos: int | None) -> str:
     """
     横轴大整数刻度：用「万」「亿」表述，避免 matplotlib 默认 ``1e6`` 科学计数法。
-    用于评价量、条数等非负计数。
+    用于销量、评价量、条数等非负计数。
     """
     if not math.isfinite(x):
         return ""
@@ -589,20 +595,20 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]:
                     p = _float_price_from_cell(str(s.get(k) or ""))
                     if p is not None:
                         break
-                rev = _reviews_volume_int(str(s.get("comment_fuzzy") or ""))
+                sales = _cn_volume_int(str(s.get("total_sales") or ""))
-                rows_data.append((label, p, rev))
+                rows_data.append((label, p, sales))
             rows_data.sort(key=lambda x: x[0])
             if not rows_data:
                 continue
             if not any(
-                (pr is not None and pr > 0) or rv > 0
+                (pr is not None and pr > 0) or sv > 0
-                for _, pr, rv in rows_data
+                for _, pr, sv in rows_data
             ):
                 continue
             n = len(rows_data)
             labels_mx = [x[0] for x in rows_data]
             prices_mx = [x[1] for x in rows_data]
-            reviews_mx = [x[2] for x in rows_data]
+            sales_mx = [x[2] for x in rows_data]
             y_pos = list(range(n))
             fig_h = max(3.4, min(14.0, 0.38 * n + 2.4))
             fig, (ax_l, ax_r) = plt.subplots(
@@ -616,21 +622,21 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]:
             ax_l.invert_yaxis()
             ax_l.set_xlabel("展示价（元）", fontsize=9)
             ax_l.set_title("展示价", fontsize=10, pad=8)
-            ax_r.barh(y_pos, reviews_mx, height=0.62, color="#059669")
+            ax_r.barh(y_pos, sales_mx, height=0.62, color="#059669")
-            ax_r.set_xlabel("评价量（搜索侧，单位：条）", fontsize=9)
+            ax_r.set_xlabel("销量（搜索列表 totalSales 口径，已解析为件数）", fontsize=9)
-            ax_r.set_title("评价量 / 声量", fontsize=10, pad=8)
+            ax_r.set_title("销量", fontsize=10, pad=8)
             ax_r.xaxis.set_major_formatter(
                 FuncFormatter(_format_xaxis_int_cn)
             )
             ax_r.tick_params(axis="y", left=False, labelleft=False)
             ttl = gname[:22] if gname else "细类"
             fig.suptitle(
-                f"「{ttl}」· 竞品矩阵：价格与评价量（与 §5 表同源）",
+                f"「{ttl}」· 竞品矩阵：价格与销量（与 §5 表同源）",
                 fontsize=11,
                 y=1.01,
             )
             fig.tight_layout()
-            out_mx = out_dir / f"chart_matrix_prices_reviews__{slug}.png"
+            out_mx = out_dir / f"chart_matrix_prices_sales__{slug}.png"
             fig.savefig(out_mx, dpi=130, bbox_inches="tight")
             plt.close(fig)
             created.append(out_mx.name)

backend/pipeline/tests/test_competitor_brief.py

@@ -8,7 +8,6 @@ from pathlib import Path
 from django.conf import settings
 from django.test import SimpleTestCase
 
-from pipeline.csv_schema import infer_total_sales_from_sales_floor
 from pipeline.report_charts import _cn_volume_int
 
 
@@ -179,13 +178,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         )
         self.assertEqual(_cn_volume_int("2.5亿件"), 250_000_000)
 
-    def test_infer_total_sales_from_sales_floor(self) -> None:
-        self.assertEqual(
-            infer_total_sales_from_sales_floor("good:99%好评 | 已售50万+"),
-            "已售50万+",
-        )
-        self.assertEqual(infer_total_sales_from_sales_floor(""), "")
-
     def test_mix_top_remainder_sums_to_all_rows(self) -> None:
         """饼图与 §4 表同源：mix_top 各 count 之和须等于含名行数。"""
         root = Path(settings.CRAWLER_JD_ROOT).resolve()

docs/examples/competitor_brief_schema_v1.json

@@ -82,7 +82,8 @@
           "shop": "示例店",
           "category": "休闲食品 > 饼干 > 粗粮饼干",
           "selling_point": "",
-          "comment_fuzzy": "20万+"
+          "comment_fuzzy": "1万+",
+          "total_sales": "已售50万+ | good:99%好评"
         }
       ]
     }