refactor(dataset): 类目筛选改为报告矩阵细类名（report_group）

新增 matrix_group_label 与 jd_competitor_report 同源路径解析；API 使用 report_group 字符串；摘要返回 report_group_options；搜索行按 SKU 从合并表回填细类；导出含报告细类列；前端下拉展示饼干/米等名称。

Made-with: Cursor

backend/pipeline/dataset_api.py

@@ -1,18 +1,35 @@
-"""库内数据浏览 API：排序、价格与类目筛选（查询参数解析与 QuerySet 变换）。"""
+"""库内数据浏览 API：排序、价格与报告细类筛选（查询参数解析与 QuerySet 变换）。"""
 from __future__ import annotations
 
 from typing import Any
 
-from django.db.models import F, QuerySet
+from django.db.models import F, Q, QuerySet
 from django.db.models.expressions import OrderBy
 from rest_framework.request import Request
 
-SEARCH_SORT_FIELDS = frozenset({"row_index", "price", "sku_id", "title", "leaf_category"})
+SEARCH_SORT_FIELDS = frozenset(
+    {"row_index", "price", "sku_id", "title", "leaf_category", "matrix_group_label"}
+)
 DETAIL_SORT_FIELDS = frozenset(
-    {"row_index", "price", "sku_id", "detail_category_path", "detail_brand"}
+    {
+        "row_index",
+        "price",
+        "sku_id",
+        "detail_category_path",
+        "detail_brand",
+        "matrix_group_label",
+    }
 )
 MERGED_SORT_FIELDS = frozenset(
-    {"row_index", "price", "sku_id", "title", "leaf_category", "detail_category_path"}
+    {
+        "row_index",
+        "price",
+        "sku_id",
+        "title",
+        "leaf_category",
+        "detail_category_path",
+        "matrix_group_label",
+    }
 )
 
 
@@ -39,11 +56,9 @@ def price_bounds_from_request(request: Request) -> tuple[float | None, float | N
     )
 
 
-def category_norm_id_from_request(request: Request) -> int | None:
+def report_group_from_request(request: Request) -> str:
-    raw = (request.query_params.get("category_norm_id") or "").strip()
+    """与 §5 矩阵一致的细类名（如「饼干」「米」）；对应查询参数 ``report_group``。"""
-    if raw.isdigit():
+    return (request.query_params.get("report_group") or "").strip()
-        return int(raw)
-    return None
 
 
 def detail_category_q_from_request(request: Request) -> str:
@@ -52,7 +67,7 @@ def detail_category_q_from_request(request: Request) -> str:
 
 def filter_echo(
     *,
-    category_norm_id: int | None,
+    report_group: str,
     price_min: float | None,
     price_max: float | None,
     detail_category_q: str,
@@ -60,7 +75,7 @@ def filter_echo(
     desc: bool,
 ) -> dict[str, Any]:
     return {
-        "category_norm_id": category_norm_id,
+        "report_group": report_group or None,
         "price_min": price_min,
         "price_max": price_max,
         "detail_category_q": detail_category_q or None,
@@ -70,9 +85,9 @@ def filter_echo(
 
 
 def apply_search_filters(qs: QuerySet, request: Request) -> QuerySet:
-    cid = category_norm_id_from_request(request)
+    rg = report_group_from_request(request)
-    if cid is not None:
+    if rg:
-        qs = qs.filter(leaf_category_norm_id=cid)
+        qs = qs.filter(Q(matrix_group_label=rg) | Q(leaf_category=rg))
     pmin, pmax = price_bounds_from_request(request)
     if pmin is not None:
         qs = qs.filter(price_value__gte=pmin)
@@ -94,6 +109,7 @@ def apply_search_order(qs: QuerySet, sort: str, desc: bool) -> QuerySet:
         "sku_id": "sku_id",
         "title": "title",
         "leaf_category": "leaf_category",
+        "matrix_group_label": "matrix_group_label",
     }[sort]
     return qs.order_by(
         OrderBy(F(field), descending=desc, nulls_last=True),
@@ -102,6 +118,9 @@ def apply_search_order(qs: QuerySet, sort: str, desc: bool) -> QuerySet:
 
 
 def apply_detail_filters(qs: QuerySet, request: Request) -> QuerySet:
+    rg = report_group_from_request(request)
+    if rg:
+        qs = qs.filter(matrix_group_label=rg)
     q = detail_category_q_from_request(request)
     if q:
         qs = qs.filter(detail_category_path__icontains=q)
@@ -126,6 +145,7 @@ def apply_detail_order(qs: QuerySet, sort: str, desc: bool) -> QuerySet:
         "sku_id": "sku_id",
         "detail_category_path": "detail_category_path",
         "detail_brand": "detail_brand",
+        "matrix_group_label": "matrix_group_label",
     }[sort]
     return qs.order_by(
         OrderBy(F(field), descending=desc, nulls_last=True),
@@ -134,9 +154,9 @@ def apply_detail_order(qs: QuerySet, sort: str, desc: bool) -> QuerySet:
 
 
 def apply_merged_filters(qs: QuerySet, request: Request) -> QuerySet:
-    cid = category_norm_id_from_request(request)
+    rg = report_group_from_request(request)
-    if cid is not None:
+    if rg:
-        qs = qs.filter(leaf_category_norm_id=cid)
+        qs = qs.filter(matrix_group_label=rg)
     q = detail_category_q_from_request(request)
     if q:
         qs = qs.filter(detail_category_path__icontains=q)
@@ -162,6 +182,7 @@ def apply_merged_order(qs: QuerySet, sort: str, desc: bool) -> QuerySet:
         "title": "title",
         "leaf_category": "leaf_category",
         "detail_category_path": "detail_category_path",
+        "matrix_group_label": "matrix_group_label",
     }[sort]
     return qs.order_by(
         OrderBy(F(field), descending=desc, nulls_last=True),

backend/pipeline/dataset_nonempty.py

@@ -14,6 +14,8 @@ from .csv_schema import (
 from .models import JdJobCommentRow, JdJobDetailRow, JdJobMergedRow, JdJobSearchRow, PipelineJob
 from .row_serialize import COMMENT_FIELDS_ORDER, DETAIL_FIELDS_ORDER
 
+MATRIX_GROUP_COLUMN = {"key": "matrix_group_label", "label": "报告细类（§5矩阵）"}
+
 
 def _is_nonempty(val) -> bool:
     if val is None:
@@ -62,7 +64,13 @@ def nonempty_merged_fields_for_job(job: PipelineJob) -> list[str]:
 
 
 def search_columns_for_api(job: PipelineJob) -> list[dict[str, str]]:
-    return [{"key": k, "label": JD_SEARCH_CSV_HEADERS[k]} for k in nonempty_search_keys_for_job(job)]
+    cols = [
+        {"key": k, "label": JD_SEARCH_CSV_HEADERS[k]}
+        for k in nonempty_search_keys_for_job(job)
+    ]
+    if JdJobSearchRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        cols.append(dict(MATRIX_GROUP_COLUMN))
+    return cols
 
 
 def _detail_field_to_csv_col(field: str) -> str:
@@ -73,10 +81,13 @@ def _detail_field_to_csv_col(field: str) -> str:
 
 
 def detail_columns_for_api(job: PipelineJob) -> list[dict[str, str]]:
-    return [
+    cols = [
         {"key": f, "label": _detail_field_to_csv_col(f)}
         for f in nonempty_detail_fields_for_job(job)
     ]
+    if JdJobDetailRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        cols.append(dict(MATRIX_GROUP_COLUMN))
+    return cols
 
 
 def _comment_field_to_csv_col(field: str) -> str:
@@ -94,21 +105,30 @@ def comment_columns_for_api(job: PipelineJob) -> list[dict[str, str]]:
 
 
 def merged_columns_for_api(job: PipelineJob) -> list[dict[str, str]]:
-    return [
+    cols = [
         {"key": k, "label": MERGED_FIELD_TO_CSV_HEADER[k]}
         for k in nonempty_merged_fields_for_job(job)
     ]
+    if JdJobMergedRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        cols.append(dict(MATRIX_GROUP_COLUMN))
+    return cols
 
 
 def search_export_headers(job: PipelineJob) -> list[str]:
     keys = nonempty_search_keys_for_job(job)
-    return ["id", "row_index"] + [JD_SEARCH_CSV_HEADERS[k] for k in keys]
+    h = ["id", "row_index"] + [JD_SEARCH_CSV_HEADERS[k] for k in keys]
+    if JdJobSearchRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        h.append(MATRIX_GROUP_COLUMN["label"])
+    return h
 
 
 def detail_export_headers(job: PipelineJob) -> list[str]:
     fields = set(nonempty_detail_fields_for_job(job))
     cols = [c for c in DETAIL_CSV_COLUMNS if DETAIL_CSV_TO_FIELD[c] in fields]
-    return ["id", "row_index"] + cols
+    h = ["id", "row_index"] + cols
+    if JdJobDetailRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        h.append(MATRIX_GROUP_COLUMN["label"])
+    return h
 
 
 def comment_export_headers(job: PipelineJob) -> list[str]:
@@ -119,4 +139,7 @@ def comment_export_headers(job: PipelineJob) -> list[str]:
 
 def merged_export_headers(job: PipelineJob) -> list[str]:
     keys = nonempty_merged_fields_for_job(job)
-    return ["id", "row_index"] + [MERGED_FIELD_TO_CSV_HEADER[k] for k in keys]
+    h = ["id", "row_index"] + [MERGED_FIELD_TO_CSV_HEADER[k] for k in keys]
+    if JdJobMergedRow.objects.filter(job=job).exclude(matrix_group_label="").exists():
+        h.append(MATRIX_GROUP_COLUMN["label"])
+    return h

backend/pipeline/export_job.py

@@ -16,6 +16,7 @@ from .csv_schema import (
     MERGED_FIELD_TO_CSV_HEADER,
 )
 from .dataset_nonempty import (
+    MATRIX_GROUP_COLUMN,
     comment_export_headers,
     detail_export_headers,
     merged_export_headers,
@@ -34,20 +35,30 @@ from .row_serialize import (
 )
 
 
-def _search_row_csv_dict(r: JdJobSearchRow, internal_keys: list[str]) -> dict[str, Any]:
+def _search_row_csv_dict(
+    r: JdJobSearchRow, internal_keys: list[str], headers: list[str]
+) -> dict[str, Any]:
     d = search_row_to_dict(r)
     out: dict[str, Any] = {"id": d["id"], "row_index": d["row_index"]}
     for k in internal_keys:
         out[JD_SEARCH_CSV_HEADERS[k]] = d.get(k, "")
+    zh = MATRIX_GROUP_COLUMN["label"]
+    if zh in headers:
+        out[zh] = d.get("matrix_group_label", "")
     return out
 
 
-def _detail_row_csv_dict(r: JdJobDetailRow, csv_cols: list[str]) -> dict[str, Any]:
+def _detail_row_csv_dict(
+    r: JdJobDetailRow, csv_cols: list[str], headers: list[str]
+) -> dict[str, Any]:
     d = detail_row_to_dict(r)
     out: dict[str, Any] = {"id": d["id"], "row_index": d["row_index"]}
     for col in csv_cols:
         fn = DETAIL_CSV_TO_FIELD[col]
         out[col] = d.get(fn, "")
+    zh = MATRIX_GROUP_COLUMN["label"]
+    if zh in headers:
+        out[zh] = d.get("matrix_group_label", "")
     return out
 
 
@@ -60,11 +71,16 @@ def _comment_row_csv_dict(r: JdJobCommentRow, csv_cols: list[str]) -> dict[str,
     return out
 
 
-def _merged_row_csv_dict(r: JdJobMergedRow, internal_keys: list[str]) -> dict[str, Any]:
+def _merged_row_csv_dict(
+    r: JdJobMergedRow, internal_keys: list[str], headers: list[str]
+) -> dict[str, Any]:
     d = merged_row_to_dict(r)
     out: dict[str, Any] = {"id": d["id"], "row_index": d["row_index"]}
     for k in internal_keys:
         out[MERGED_FIELD_TO_CSV_HEADER[k]] = d.get(k, "")
+    zh = MATRIX_GROUP_COLUMN["label"]
+    if zh in headers:
+        out[zh] = d.get("matrix_group_label", "")
     return out
 
 
@@ -98,20 +114,30 @@ def _prune_merged_dict(d: dict[str, Any], fields: list[str]) -> dict[str, Any]:
 
 def _rows_as_list_search(job: PipelineJob) -> list[dict[str, Any]]:
     keys = nonempty_search_keys_for_job(job)
+    extra_mg = JdJobSearchRow.objects.filter(job=job).exclude(matrix_group_label="").exists()
     qs = JdJobSearchRow.objects.filter(job=job)
-    return [
+    out: list[dict[str, Any]] = []
-        _prune_search_dict(search_row_to_dict(obj), keys)
+    for obj in qs.order_by("row_index").iterator(chunk_size=400):
-        for obj in qs.order_by("row_index").iterator(chunk_size=400)
+        d = search_row_to_dict(obj)
-    ]
+        row = _prune_search_dict(d, keys)
+        if extra_mg:
+            row["matrix_group_label"] = d.get("matrix_group_label", "")
+        out.append(row)
+    return out
 
 
 def _rows_as_list_detail(job: PipelineJob) -> list[dict[str, Any]]:
     fields = nonempty_detail_fields_for_job(job)
+    extra_mg = JdJobDetailRow.objects.filter(job=job).exclude(matrix_group_label="").exists()
     qs = JdJobDetailRow.objects.filter(job=job)
-    return [
+    out: list[dict[str, Any]] = []
-        _prune_detail_dict(detail_row_to_dict(obj), fields)
+    for obj in qs.order_by("row_index").iterator(chunk_size=400):
-        for obj in qs.order_by("row_index").iterator(chunk_size=400)
+        d = detail_row_to_dict(obj)
-    ]
+        row = _prune_detail_dict(d, fields)
+        if extra_mg:
+            row["matrix_group_label"] = d.get("matrix_group_label", "")
+        out.append(row)
+    return out
 
 
 def _rows_as_list_comment(job: PipelineJob) -> list[dict[str, Any]]:
@@ -125,11 +151,16 @@ def _rows_as_list_comment(job: PipelineJob) -> list[dict[str, Any]]:
 
 def _rows_as_list_merged(job: PipelineJob) -> list[dict[str, Any]]:
     fields = nonempty_merged_fields_for_job(job)
+    extra_mg = JdJobMergedRow.objects.filter(job=job).exclude(matrix_group_label="").exists()
     qs = JdJobMergedRow.objects.filter(job=job)
-    return [
+    out: list[dict[str, Any]] = []
-        _prune_merged_dict(merged_row_to_dict(obj), fields)
+    for obj in qs.order_by("row_index").iterator(chunk_size=400):
-        for obj in qs.order_by("row_index").iterator(chunk_size=400)
+        d = merged_row_to_dict(obj)
-    ]
+        row = _prune_merged_dict(d, fields)
+        if extra_mg:
+            row["matrix_group_label"] = d.get("matrix_group_label", "")
+        out.append(row)
+    return out
 
 
 def build_json_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
@@ -178,18 +209,21 @@ def _write_csv_from_qs(
 def build_csv_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
     if kind == "search":
         sk = nonempty_search_keys_for_job(job)
+        headers = search_export_headers(job)
         text = _write_csv_from_qs(
             qs=JdJobSearchRow.objects.filter(job=job),
-            headers=search_export_headers(job),
+            headers=headers,
-            row_fn=lambda o, _sk=sk: _search_row_csv_dict(o, _sk),
+            row_fn=lambda o, _sk=sk, _h=headers: _search_row_csv_dict(o, _sk, _h),
         )
         name = f"job_{job.id}_search.csv"
     elif kind == "detail":
-        dcols = [c for c in detail_export_headers(job) if c not in ("id", "row_index")]
+        headers = detail_export_headers(job)
+        zh = MATRIX_GROUP_COLUMN["label"]
+        dcols = [c for c in headers if c not in ("id", "row_index", zh)]
         text = _write_csv_from_qs(
             qs=JdJobDetailRow.objects.filter(job=job),
-            headers=detail_export_headers(job),
+            headers=headers,
-            row_fn=lambda o, _dc=dcols: _detail_row_csv_dict(o, _dc),
+            row_fn=lambda o, _dc=dcols, _h=headers: _detail_row_csv_dict(o, _dc, _h),
         )
         name = f"job_{job.id}_detail.csv"
     elif kind == "comments":
@@ -202,22 +236,28 @@ def build_csv_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
         name = f"job_{job.id}_comments.csv"
     elif kind == "all":
         sk = nonempty_search_keys_for_job(job)
-        dcols = [c for c in detail_export_headers(job) if c not in ("id", "row_index")]
+        sheaders = search_export_headers(job)
+        dheaders = detail_export_headers(job)
+        zh = MATRIX_GROUP_COLUMN["label"]
+        dcols = [c for c in dheaders if c not in ("id", "row_index", zh)]
         ccols = [c for c in comment_export_headers(job) if c not in ("id", "row_index")]
         mk = nonempty_merged_fields_for_job(job)
+        mheaders = merged_export_headers(job)
         parts = [
             "# search",
             _write_csv_from_qs(
                 qs=JdJobSearchRow.objects.filter(job=job),
-                headers=search_export_headers(job),
+                headers=sheaders,
-                row_fn=lambda o, _sk=sk: _search_row_csv_dict(o, _sk),
+                row_fn=lambda o, _sk=sk, _h=sheaders: _search_row_csv_dict(o, _sk, _h),
             ),
             "",
             "# detail",
             _write_csv_from_qs(
                 qs=JdJobDetailRow.objects.filter(job=job),
-                headers=detail_export_headers(job),
+                headers=dheaders,
-                row_fn=lambda o, _dc=dcols: _detail_row_csv_dict(o, _dc),
+                row_fn=lambda o, _dc=dcols, _h=dheaders: _detail_row_csv_dict(
+                    o, _dc, _h
+                ),
             ),
             "",
             "# comments",
@@ -230,18 +270,19 @@ def build_csv_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
             "# merged",
             _write_csv_from_qs(
                 qs=JdJobMergedRow.objects.filter(job=job),
-                headers=merged_export_headers(job),
+                headers=mheaders,
-                row_fn=lambda o, _mk=mk: _merged_row_csv_dict(o, _mk),
+                row_fn=lambda o, _mk=mk, _h=mheaders: _merged_row_csv_dict(o, _mk, _h),
             ),
         ]
         text = "\n".join(parts)
         name = f"job_{job.id}_all.csv"
     elif kind == "merged":
         mk = nonempty_merged_fields_for_job(job)
+        headers = merged_export_headers(job)
         text = _write_csv_from_qs(
             qs=JdJobMergedRow.objects.filter(job=job),
-            headers=merged_export_headers(job),
+            headers=headers,
-            row_fn=lambda o, _mk=mk: _merged_row_csv_dict(o, _mk),
+            row_fn=lambda o, _mk=mk, _h=headers: _merged_row_csv_dict(o, _mk, _h),
         )
         name = f"job_{job.id}_merged.csv"
     else:
@@ -262,22 +303,25 @@ def build_xlsx_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
         ws = wb.active
         ws.title = "search"[:31]
         sk = nonempty_search_keys_for_job(job)
+        sheaders = search_export_headers(job)
         _append_sheet(
             ws,
-            search_export_headers(job),
+            sheaders,
             JdJobSearchRow.objects.filter(job=job),
-            lambda o, _sk=sk: _search_row_csv_dict(o, _sk),
+            lambda o, _sk=sk, _h=sheaders: _search_row_csv_dict(o, _sk, _h),
         )
         name = f"job_{job.id}_search.xlsx"
     elif kind == "detail":
         ws = wb.active
         ws.title = "detail"[:31]
-        dcols = [c for c in detail_export_headers(job) if c not in ("id", "row_index")]
+        dheaders = detail_export_headers(job)
+        zh = MATRIX_GROUP_COLUMN["label"]
+        dcols = [c for c in dheaders if c not in ("id", "row_index", zh)]
         _append_sheet(
             ws,
-            detail_export_headers(job),
+            dheaders,
             JdJobDetailRow.objects.filter(job=job),
-            lambda o, _dc=dcols: _detail_row_csv_dict(o, _dc),
+            lambda o, _dc=dcols, _h=dheaders: _detail_row_csv_dict(o, _dc, _h),
         )
         name = f"job_{job.id}_detail.xlsx"
     elif kind == "comments":
@@ -293,23 +337,27 @@ def build_xlsx_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
         name = f"job_{job.id}_comments.xlsx"
     elif kind == "all":
         sk = nonempty_search_keys_for_job(job)
-        dcols = [c for c in detail_export_headers(job) if c not in ("id", "row_index")]
+        sheaders = search_export_headers(job)
+        dheaders = detail_export_headers(job)
+        zh = MATRIX_GROUP_COLUMN["label"]
+        dcols = [c for c in dheaders if c not in ("id", "row_index", zh)]
         ccols = [c for c in comment_export_headers(job) if c not in ("id", "row_index")]
         mk = nonempty_merged_fields_for_job(job)
+        mheaders = merged_export_headers(job)
         ws1 = wb.active
         ws1.title = "search"[:31]
         _append_sheet(
             ws1,
-            search_export_headers(job),
+            sheaders,
             JdJobSearchRow.objects.filter(job=job),
-            lambda o, _sk=sk: _search_row_csv_dict(o, _sk),
+            lambda o, _sk=sk, _h=sheaders: _search_row_csv_dict(o, _sk, _h),
         )
         ws2 = wb.create_sheet("detail"[:31])
         _append_sheet(
             ws2,
-            detail_export_headers(job),
+            dheaders,
             JdJobDetailRow.objects.filter(job=job),
-            lambda o, _dc=dcols: _detail_row_csv_dict(o, _dc),
+            lambda o, _dc=dcols, _h=dheaders: _detail_row_csv_dict(o, _dc, _h),
         )
         ws3 = wb.create_sheet("comments"[:31])
         _append_sheet(
@@ -321,20 +369,21 @@ def build_xlsx_bytes(*, job: PipelineJob, kind: str) -> tuple[bytes, str]:
         ws4 = wb.create_sheet("merged"[:31])
         _append_sheet(
             ws4,
-            merged_export_headers(job),
+            mheaders,
             JdJobMergedRow.objects.filter(job=job),
-            lambda o, _mk=mk: _merged_row_csv_dict(o, _mk),
+            lambda o, _mk=mk, _h=mheaders: _merged_row_csv_dict(o, _mk, _h),
         )
         name = f"job_{job.id}_all.xlsx"
     elif kind == "merged":
         mk = nonempty_merged_fields_for_job(job)
         ws = wb.active
         ws.title = "merged"[:31]
+        mheaders = merged_export_headers(job)
         _append_sheet(
             ws,
-            merged_export_headers(job),
+            mheaders,
             JdJobMergedRow.objects.filter(job=job),
-            lambda o, _mk=mk: _merged_row_csv_dict(o, _mk),
+            lambda o, _mk=mk, _h=mheaders: _merged_row_csv_dict(o, _mk, _h),
         )
         name = f"job_{job.id}_merged.xlsx"
     else:

backend/pipeline/ingest.py

@@ -29,12 +29,12 @@ from .csv_schema import (
     search_csv_effective_total_sales,
     strip_buyer_ranking_line_prefix,
 )
+from .matrix_group_label import matrix_group_label_from_detail_path
 from .models import (
     JdJobCommentRow,
     JdJobDetailRow,
     JdJobMergedRow,
     JdJobSearchRow,
-    JdLeafCategoryNorm,
     JdProduct,
     JdProductSnapshot,
     PipelineJob,
@@ -119,6 +119,33 @@ def _bulk_create_in_chunks(model, objects: list[Any]) -> None:
         model.objects.bulk_create(objects[i : i + BULK_CHUNK])
 
 
+def _sync_search_rows_matrix_labels(
+    job: PipelineJob, merged_kw_list: list[tuple[int, dict[str, str]]]
+) -> None:
+    """按 SKU 将合并表解析出的报告细类回填到搜索行（与 §5 矩阵口径一致）。"""
+    sku_to_mg: dict[str, str] = {}
+    for _, kw in merged_kw_list:
+        sk = (kw.get("sku_id") or "").strip()
+        if not sk:
+            continue
+        mg = matrix_group_label_from_detail_path(kw.get("detail_category_path") or "")
+        if mg:
+            sku_to_mg[sk] = mg
+    if not sku_to_mg:
+        return
+    chunk: list[JdJobSearchRow] = []
+    for r in JdJobSearchRow.objects.filter(job=job).iterator(chunk_size=400):
+        sk = (r.sku_id or "").strip()
+        if sk and sk in sku_to_mg:
+            r.matrix_group_label = sku_to_mg[sk]
+            chunk.append(r)
+        if len(chunk) >= 400:
+            JdJobSearchRow.objects.bulk_update(chunk, ["matrix_group_label"])
+            chunk.clear()
+    if chunk:
+        JdJobSearchRow.objects.bulk_update(chunk, ["matrix_group_label"])
+
+
 def _run_dir(job: PipelineJob) -> Path:
     return Path(job.run_dir or "").expanduser().resolve()
 
@@ -174,32 +201,12 @@ def ingest_job_dataset_rows(job: PipelineJob) -> dict[str, Any]:
     if not search_rows and search_path.is_file() is False:
         pass
     search_kw_list: list[tuple[int, dict[str, str]]] = []
-    leaf_labels: set[str] = set()
     for i, row in enumerate(search_rows):
         _normalize_search_csv_total_sales(row)
         kw = _search_row_kwargs(row)
         search_kw_list.append((i, kw))
-        lc = (kw.get("leaf_category") or "").strip()[:512]
-        if lc:
-            leaf_labels.add(lc)
-    norm_map: dict[str, JdLeafCategoryNorm] = {}
-    if leaf_labels:
-        have = set(
-            JdLeafCategoryNorm.objects.filter(label__in=leaf_labels).values_list(
-                "label", flat=True
-            )
-        )
-        missing = [JdLeafCategoryNorm(label=l) for l in leaf_labels if l not in have]
-        if missing:
-            JdLeafCategoryNorm.objects.bulk_create(missing, ignore_conflicts=True)
-        norm_map = {
-            n.label: n
-            for n in JdLeafCategoryNorm.objects.filter(label__in=leaf_labels)
-        }
     s_objs: list[JdJobSearchRow] = []
     for i, kw in search_kw_list:
-        lc = (kw.get("leaf_category") or "").strip()[:512]
-        norm = norm_map.get(lc) if lc else None
         pv = effective_list_price_value(
             kw.get("coupon_price"), kw.get("price"), kw.get("original_price")
         )
@@ -207,7 +214,7 @@ def ingest_job_dataset_rows(job: PipelineJob) -> dict[str, Any]:
             JdJobSearchRow(
                 job=job,
                 row_index=i,
-                leaf_category_norm=norm,
+                matrix_group_label="",
                 price_value=pv,
                 **kw,
             )
@@ -221,9 +228,14 @@ def ingest_job_dataset_rows(job: PipelineJob) -> dict[str, Any]:
     for i, row in enumerate(detail_rows):
         kw = _detail_row_kwargs(row)
         dpv = float_price_from_cell(kw.get("detail_price_final"))
+        mg = matrix_group_label_from_detail_path(kw.get("detail_category_path") or "")
         d_objs.append(
             JdJobDetailRow(
-                job=job, row_index=i, detail_price_value=dpv, **kw
+                job=job,
+                row_index=i,
+                matrix_group_label=mg,
+                detail_price_value=dpv,
+                **kw,
             )
         )
     _bulk_create_in_chunks(JdJobDetailRow, d_objs)
@@ -241,34 +253,13 @@ def ingest_job_dataset_rows(job: PipelineJob) -> dict[str, Any]:
     merged_path = run_dir / FILE_MERGED_CSV
     merged_rows = _read_csv_rows(merged_path) if merged_path.is_file() else []
     merged_kw_list: list[tuple[int, dict[str, str]]] = []
-    leaf_labels_m: set[str] = set()
     for i, row in enumerate(merged_rows):
         _normalize_merged_csv_total_sales(row)
         kw = _merged_row_kwargs(row)
         merged_kw_list.append((i, kw))
-        lc = (kw.get("leaf_category") or "").strip()[:512]
-        if lc:
-            leaf_labels_m.add(lc)
-    norm_map_m: dict[str, JdLeafCategoryNorm] = {}
-    if leaf_labels_m:
-        have_m = set(
-            JdLeafCategoryNorm.objects.filter(label__in=leaf_labels_m).values_list(
-                "label", flat=True
-            )
-        )
-        missing_m = [
-            JdLeafCategoryNorm(label=l) for l in leaf_labels_m if l not in have_m
-        ]
-        if missing_m:
-            JdLeafCategoryNorm.objects.bulk_create(missing_m, ignore_conflicts=True)
-        norm_map_m = {
-            n.label: n
-            for n in JdLeafCategoryNorm.objects.filter(label__in=leaf_labels_m)
-        }
     m_objs: list[JdJobMergedRow] = []
     for i, kw in merged_kw_list:
-        lc = (kw.get("leaf_category") or "").strip()[:512]
+        mg = matrix_group_label_from_detail_path(kw.get("detail_category_path") or "")
-        norm = norm_map_m.get(lc) if lc else None
         pv = effective_list_price_value(
             kw.get("coupon_price"), kw.get("price"), kw.get("original_price")
         )
@@ -276,13 +267,14 @@ def ingest_job_dataset_rows(job: PipelineJob) -> dict[str, Any]:
             JdJobMergedRow(
                 job=job,
                 row_index=i,
-                leaf_category_norm=norm,
+                matrix_group_label=mg,
                 price_value=pv,
                 **kw,
             )
         )
     _bulk_create_in_chunks(JdJobMergedRow, m_objs)
     stats["merged_table_rows"] = len(m_objs)
+    _sync_search_rows_matrix_labels(job, merged_kw_list)
 
     return stats
 

backend/pipeline/matrix_group_label.py

@@ -0,0 +1,63 @@
+"""
+与 ``jd_competitor_report._matrix_group_label_from_path`` 同源：
+从商详 ``detail_category_path`` 解析 §5 竞品矩阵用的细类展示名（如「饼干」「米」）。
+"""
+from __future__ import annotations
+
+import re
+
+
+def category_token_meaningless(seg: str) -> bool:
+    """纯数字类目 ID、空串或疑似内部编码的段，不宜直接作为矩阵分组展示名。"""
+    t = (seg or "").strip()
+    if not t:
+        return True
+    if t.isdigit():
+        return True
+    if len(t) >= 14 and re.fullmatch(r"[A-Za-z0-9_\-]+", t):
+        return True
+    return False
+
+
+def matrix_display_segment_from_parts(parts: list[str]) -> str | None:
+    """
+    与历史逻辑一致的主选段；若该段无意义则自右向左找第一段可读文本
+    （避免「仅类目码」或中间段为数字 ID 时整组成品名式乱桶）。
+    """
+    if not parts:
+        return None
+    if len(parts) >= 4:
+        preferred = parts[-2]
+    elif len(parts) >= 3:
+        preferred = parts[1]
+    elif len(parts) >= 2:
+        preferred = parts[1]
+    else:
+        preferred = parts[0]
+    order: list[str] = []
+    if preferred:
+        order.append(preferred)
+    if len(parts) >= 2:
+        order.append(parts[-2])
+    order.append(parts[-1])
+    order.extend(reversed(parts))
+    seen: set[str] = set()
+    for cand in order:
+        if not cand or cand in seen:
+            continue
+        seen.add(cand)
+        if not category_token_meaningless(cand):
+            return cand.strip()
+    return None
+
+
+def matrix_group_label_from_detail_path(path: str) -> str:
+    """由 ``detail_category_path`` 文本解析细类展示名；空或无可读段则返回空串。"""
+    t = (path or "").strip()
+    if not t:
+        return ""
+    parts = [p.strip() for p in t.replace("＞", ">").split(">") if p.strip()]
+    if not parts:
+        return ""
+    key = matrix_display_segment_from_parts(parts)
+    return (key[:80] if key else "")

backend/pipeline/migrations/0018_report_group_matrix_label.py

@@ -0,0 +1,92 @@
+# Generated by Django 5.2.1 on 2026-04-16 01:55
+
+from django.db import migrations, models
+
+
+def backfill_matrix_group_labels(apps, schema_editor):
+    from pipeline.matrix_group_label import matrix_group_label_from_detail_path
+
+    JdJobDetailRow = apps.get_model("pipeline", "JdJobDetailRow")
+    JdJobMergedRow = apps.get_model("pipeline", "JdJobMergedRow")
+    JdJobSearchRow = apps.get_model("pipeline", "JdJobSearchRow")
+
+    chunk: list = []
+    for r in JdJobDetailRow.objects.all().iterator(chunk_size=400):
+        r.matrix_group_label = matrix_group_label_from_detail_path(
+            r.detail_category_path or ""
+        )
+        chunk.append(r)
+        if len(chunk) >= 400:
+            JdJobDetailRow.objects.bulk_update(chunk, ["matrix_group_label"])
+            chunk.clear()
+    if chunk:
+        JdJobDetailRow.objects.bulk_update(chunk, ["matrix_group_label"])
+
+    chunk = []
+    for r in JdJobMergedRow.objects.all().iterator(chunk_size=400):
+        r.matrix_group_label = matrix_group_label_from_detail_path(
+            r.detail_category_path or ""
+        )
+        chunk.append(r)
+        if len(chunk) >= 400:
+            JdJobMergedRow.objects.bulk_update(chunk, ["matrix_group_label"])
+            chunk.clear()
+    if chunk:
+        JdJobMergedRow.objects.bulk_update(chunk, ["matrix_group_label"])
+
+    sku_to_mg: dict[str, str] = {}
+    for r in JdJobMergedRow.objects.exclude(matrix_group_label="").iterator(
+        chunk_size=400
+    ):
+        sk = str(r.sku_id or "").strip()
+        if sk:
+            sku_to_mg[sk] = r.matrix_group_label
+
+    chunk = []
+    for r in JdJobSearchRow.objects.all().iterator(chunk_size=400):
+        sk = str(r.sku_id or "").strip()
+        r.matrix_group_label = sku_to_mg.get(sk, "")
+        chunk.append(r)
+        if len(chunk) >= 400:
+            JdJobSearchRow.objects.bulk_update(chunk, ["matrix_group_label"])
+            chunk.clear()
+    if chunk:
+        JdJobSearchRow.objects.bulk_update(chunk, ["matrix_group_label"])
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('pipeline', '0017_dataset_browse_filters'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='jdjobdetailrow',
+            name='matrix_group_label',
+            field=models.CharField(blank=True, db_index=True, default='', help_text='与 §5 矩阵同源：由 detail_category_path 解析', max_length=80, verbose_name='报告细类'),
+        ),
+        migrations.AddField(
+            model_name='jdjobmergedrow',
+            name='matrix_group_label',
+            field=models.CharField(blank=True, db_index=True, default='', help_text='与 §5 矩阵同源：由 detail_category_path 解析', max_length=80, verbose_name='报告细类'),
+        ),
+        migrations.AddField(
+            model_name='jdjobsearchrow',
+            name='matrix_group_label',
+            field=models.CharField(blank=True, db_index=True, default='', help_text='与 §5 矩阵同源：由合并表商详路径解析；可按 SKU 从合并表回填', max_length=80, verbose_name='报告细类'),
+        ),
+        migrations.AddIndex(
+            model_name='jdjobdetailrow',
+            index=models.Index(fields=['job', 'matrix_group_label'], name='pipeline_jd_job_id_5595d3_idx'),
+        ),
+        migrations.AddIndex(
+            model_name='jdjobmergedrow',
+            index=models.Index(fields=['job', 'matrix_group_label'], name='pipeline_jd_job_id_163e3f_idx'),
+        ),
+        migrations.AddIndex(
+            model_name='jdjobsearchrow',
+            index=models.Index(fields=['job', 'matrix_group_label'], name='pipeline_jd_job_id_38fae5_idx'),
+        ),
+        migrations.RunPython(backfill_matrix_group_labels, migrations.RunPython.noop),
+    ]

backend/pipeline/models.py

@@ -190,6 +190,14 @@ class JdJobSearchRow(models.Model):
         on_delete=models.SET_NULL,
         related_name="search_rows",
     )
+    matrix_group_label = models.CharField(
+        max_length=80,
+        blank=True,
+        default="",
+        db_index=True,
+        verbose_name="报告细类",
+        help_text="与 §5 矩阵同源：由合并表商详路径解析；可按 SKU 从合并表回填",
+    )
     price_value = models.FloatField(null=True, blank=True, db_index=True)
     platform = models.TextField(blank=True, default="")
     keyword = models.TextField(blank=True, default="")
@@ -206,6 +214,7 @@ class JdJobSearchRow(models.Model):
         indexes = [
             models.Index(fields=["job", "sku_id"]),
             models.Index(fields=["job", "leaf_category_norm"]),
+            models.Index(fields=["job", "matrix_group_label"]),
             models.Index(fields=["job", "price_value"]),
         ]
 
@@ -232,6 +241,14 @@ class JdJobDetailRow(models.Model):
     buyer_ranking_line = models.TextField(blank=True, default="")
     buyer_promo_text = models.TextField(blank=True, default="")
     detail_price_value = models.FloatField(null=True, blank=True, db_index=True)
+    matrix_group_label = models.CharField(
+        max_length=80,
+        blank=True,
+        default="",
+        db_index=True,
+        verbose_name="报告细类",
+        help_text="与 §5 矩阵同源：由 detail_category_path 解析",
+    )
 
     class Meta:
         ordering = ["row_index"]
@@ -244,6 +261,7 @@ class JdJobDetailRow(models.Model):
         indexes = [
             models.Index(fields=["job", "sku_id"]),
             models.Index(fields=["job", "detail_price_value"]),
+            models.Index(fields=["job", "matrix_group_label"]),
         ]
 
     def __str__(self) -> str:
@@ -317,6 +335,14 @@ class JdJobMergedRow(models.Model):
         on_delete=models.SET_NULL,
         related_name="merged_rows",
     )
+    matrix_group_label = models.CharField(
+        max_length=80,
+        blank=True,
+        default="",
+        db_index=True,
+        verbose_name="报告细类",
+        help_text="与 §5 矩阵同源：由 detail_category_path 解析",
+    )
     price_value = models.FloatField(null=True, blank=True, db_index=True)
     keyword = models.TextField(blank=True, default="")
     page = models.TextField(blank=True, default="")
@@ -342,6 +368,7 @@ class JdJobMergedRow(models.Model):
         indexes = [
             models.Index(fields=["job", "sku_id"]),
             models.Index(fields=["job", "leaf_category_norm"]),
+            models.Index(fields=["job", "matrix_group_label"]),
             models.Index(fields=["job", "price_value"]),
         ]
 

backend/pipeline/row_serialize.py

@@ -22,6 +22,7 @@ def search_row_to_dict(r: JdJobSearchRow) -> dict[str, Any]:
     out: dict[str, Any] = {"id": r.id, "row_index": r.row_index}
     for k in JD_SEARCH_INTERNAL_KEYS:
         out[k] = getattr(r, k) or ""
+    out["matrix_group_label"] = r.matrix_group_label or ""
     return out
 
 
@@ -29,6 +30,7 @@ def detail_row_to_dict(r: JdJobDetailRow) -> dict[str, Any]:
     out: dict[str, Any] = {"id": r.id, "row_index": r.row_index}
     for k in DETAIL_FIELDS_ORDER:
         out[k] = getattr(r, k) or ""
+    out["matrix_group_label"] = r.matrix_group_label or ""
     return out
 
 
@@ -43,4 +45,5 @@ def merged_row_to_dict(r: JdJobMergedRow) -> dict[str, Any]:
     out: dict[str, Any] = {"id": r.id, "row_index": r.row_index}
     for k in MERGED_FIELDS_ORDER:
         out[k] = getattr(r, k) or ""
+    out["matrix_group_label"] = r.matrix_group_label or ""
     return out

backend/pipeline/views.py

@@ -27,11 +27,11 @@ from .dataset_api import (
     apply_merged_order,
     apply_search_filters,
     apply_search_order,
-    category_norm_id_from_request,
     detail_category_q_from_request,
     filter_echo,
     parse_sort_meta,
     price_bounds_from_request,
+    report_group_from_request,
 )
 from .dataset_nonempty import (
     comment_columns_for_api,
@@ -66,7 +66,6 @@ from .models import (
     JdJobDetailRow,
     JdJobMergedRow,
     JdJobSearchRow,
-    JdLeafCategoryNorm,
     JdProduct,
     JdProductSnapshot,
     JobStatus,
@@ -756,17 +755,15 @@ def _read_page_params(request) -> tuple[int, int]:
     return page, page_size
 
 
-def _category_norm_options_for_job(job: PipelineJob, RowModel: type) -> list[dict[str, Any]]:
+def _report_group_options_for_job(job: PipelineJob) -> list[str]:
-    ids = (
+    """与 §5 矩阵一致的细类名列表（来自合并表 ``detail_category_path`` 解析）。"""
-        RowModel.objects.filter(job=job, leaf_category_norm_id__isnull=False)
+    qs = (
-        .values_list("leaf_category_norm_id", flat=True)
+        JdJobMergedRow.objects.filter(job=job)
+        .exclude(matrix_group_label="")
+        .values_list("matrix_group_label", flat=True)
         .distinct()
     )
-    return list(
+    return sorted({str(x) for x in qs if x})
-        JdLeafCategoryNorm.objects.filter(id__in=ids)
-        .order_by("label")
-        .values("id", "label")
-    )
 
 
 def _detail_category_path_options(job: PipelineJob) -> list[str]:
@@ -797,12 +794,7 @@ class JobDatasetSummaryView(APIView):
                 "detail_columns": detail_columns_for_api(job),
                 "comment_columns": comment_columns_for_api(job),
                 "merged_columns": merged_columns_for_api(job),
-                "search_category_options": _category_norm_options_for_job(
+                "report_group_options": _report_group_options_for_job(job),
-                    job, JdJobSearchRow
-                ),
-                "merged_category_options": _category_norm_options_for_job(
-                    job, JdJobMergedRow
-                ),
                 "detail_category_path_options": _detail_category_path_options(job),
                 "dataset_sort_help": {
                     "search": sorted(SEARCH_SORT_FIELDS),
@@ -820,7 +812,7 @@ class JobDatasetSearchView(APIView):
         page, page_size = _read_page_params(request)
         sort, desc = parse_sort_meta(request)
         sort_eff = sort if sort in SEARCH_SORT_FIELDS else "row_index"
-        cid = category_norm_id_from_request(request)
+        rg = report_group_from_request(request)
         pmin, pmax = price_bounds_from_request(request)
         dcq = detail_category_q_from_request(request)
         qs = JdJobSearchRow.objects.filter(job=job)
@@ -835,7 +827,7 @@ class JobDatasetSearchView(APIView):
                 "page": page,
                 "page_size": page_size,
                 "filters": filter_echo(
-                    category_norm_id=cid,
+                    report_group=rg,
                     price_min=pmin,
                     price_max=pmax,
                     detail_category_q=dcq,
@@ -853,7 +845,7 @@ class JobDatasetDetailView(APIView):
         page, page_size = _read_page_params(request)
         sort, desc = parse_sort_meta(request)
         sort_eff = sort if sort in DETAIL_SORT_FIELDS else "row_index"
-        cid = category_norm_id_from_request(request)
+        rg = report_group_from_request(request)
         pmin, pmax = price_bounds_from_request(request)
         dcq = detail_category_q_from_request(request)
         qs = JdJobDetailRow.objects.filter(job=job)
@@ -868,7 +860,7 @@ class JobDatasetDetailView(APIView):
                 "page": page,
                 "page_size": page_size,
                 "filters": filter_echo(
-                    category_norm_id=cid,
+                    report_group=rg,
                     price_min=pmin,
                     price_max=pmax,
                     detail_category_q=dcq,
@@ -908,7 +900,7 @@ class JobDatasetMergedView(APIView):
         page, page_size = _read_page_params(request)
         sort, desc = parse_sort_meta(request)
         sort_eff = sort if sort in MERGED_SORT_FIELDS else "row_index"
-        cid = category_norm_id_from_request(request)
+        rg = report_group_from_request(request)
         pmin, pmax = price_bounds_from_request(request)
         dcq = detail_category_q_from_request(request)
         qs = JdJobMergedRow.objects.filter(job=job)
@@ -923,7 +915,7 @@ class JobDatasetMergedView(APIView):
                 "page": page,
                 "page_size": page_size,
                 "filters": filter_echo(
-                    category_norm_id=cid,
+                    report_group=rg,
                     price_min=pmin,
                     price_max=pmax,
                     detail_category_q=dcq,

frontend/src/components/JobDatasetModal.vue

@@ -24,6 +24,7 @@ const SORT_LABELS = {
   sku_id: 'SKU',
   title: '标题',
   leaf_category: '叶类目',
+  matrix_group_label: '报告细类',
   detail_category_path: '类目路径',
   detail_brand: '品牌',
 }
@@ -39,7 +40,8 @@ const err = ref('')
 const commentSkuFilter = ref('')
 const sortField = ref('row_index')
 const sortOrder = ref('asc')
-const categoryNormId = ref('')
+/** 与 §5 矩阵一致的细类名（如饼干、米），对应接口参数 report_group */
+const reportGroup = ref('')
 const priceMin = ref('')
 const priceMax = ref('')
 const detailCategoryQ = ref('')
@@ -68,12 +70,7 @@ const sortOptions = computed(() => {
   return keys.map((k) => ({ value: k, label: SORT_LABELS[k] || k }))
 })
 
-const categoryNormOptions = computed(() => {
+const reportGroupOptions = computed(() => summary.value?.report_group_options || [])
-  if (!summary.value) return []
-  if (tab.value === 'search') return summary.value.search_category_options || []
-  if (tab.value === 'merged') return summary.value.merged_category_options || []
-  return []
-})
 
 const displayColumns = computed(() => {
   const s = summary.value
@@ -164,7 +161,7 @@ async function refreshList() {
         : {
             sort: sortField.value,
             order: sortOrder.value,
-            categoryNormId: categoryNormId.value.trim(),
+            reportGroup: reportGroup.value.trim(),
             priceMin: priceMin.value,
             priceMax: priceMax.value,
             detailCategoryQ: detailCategoryQ.value.trim(),
@@ -198,7 +195,7 @@ watch(
       page.value = 1
       sortField.value = 'row_index'
       sortOrder.value = 'asc'
-      categoryNormId.value = ''
+      reportGroup.value = ''
       priceMin.value = ''
       priceMax.value = ''
       detailCategoryQ.value = ''
@@ -216,7 +213,7 @@ watch(tab, () => {
   exportPanelOpen.value = false
   sortField.value = 'row_index'
   sortOrder.value = 'asc'
-  categoryNormId.value = ''
+  reportGroup.value = ''
   priceMin.value = ''
   priceMax.value = ''
   detailCategoryQ.value = ''
@@ -226,7 +223,7 @@ watch(
   [
     sortField,
     sortOrder,
-    categoryNormId,
+    reportGroup,
     priceMin,
     priceMax,
     detailCategoryQ,
@@ -245,7 +242,7 @@ watch(
     commentSkuFilter,
     sortField,
     sortOrder,
-    categoryNormId,
+    reportGroup,
     priceMin,
     priceMax,
     detailCategoryQ,
@@ -430,17 +427,13 @@ async function runExport(format) {
               <option value="desc">降序</option>
             </select>
           </label>
-          <template v-if="tab === 'search' || tab === 'merged'">
+          <label class="filter-item">
-            <label class="filter-item">
+            报告细类（与矩阵一致）
-              叶类目
+            <select v-model="reportGroup" class="filter-select wide">
-              <select v-model="categoryNormId" class="filter-select wide">
+              <option value="">全部</option>
-                <option value="">全部</option>
+              <option v-for="g in reportGroupOptions" :key="g" :value="g">{{ g }}</option>
-                <option v-for="c in categoryNormOptions" :key="c.id" :value="String(c.id)">
+            </select>
-                  {{ c.label }}
+          </label>
-                </option>
-              </select>
-            </label>
-          </template>
           <template v-if="tab === 'detail' || tab === 'merged'">
             <label class="filter-item">
               类目路径包含

frontend/src/composables/useJobs.js

@@ -205,9 +205,10 @@ export function jobDatasetPageUrl(jobId, kind, page = 1, pageSize = 50, opts = {
   if (sku) p.set('sku_id', String(sku))
   if (o.sort) p.set('sort', String(o.sort))
   if (o.order) p.set('order', String(o.order))
-  const cn = o.categoryNormId ?? o.category_norm_id
+  const rg =
-  if (cn !== undefined && cn !== null && String(cn).trim() !== '')
+    o.reportGroup ?? o.report_group ?? o.categoryNormId ?? o.category_norm_id
-    p.set('category_norm_id', String(cn).trim())
+  if (rg !== undefined && rg !== null && String(rg).trim() !== '')
+    p.set('report_group', String(rg).trim())
   const pmin = o.priceMin ?? o.price_min
   if (pmin !== undefined && pmin !== null && String(pmin).trim() !== '')
     p.set('price_min', String(pmin).trim())