fix(report): shop/brand pie totals, matrix price charts, drop LLM §8.5 merge

Made-with: Cursor

backend/crawler_copy/jd_pc_search/jd_competitor_report.py

@@ -281,6 +281,27 @@ def _collect_prices(rows: list[dict[str, str]]) -> list[float]:
     return out
 
 
+def _label_count_dicts_top_n_plus_other(
+    labels: list[str], *, top_n: int, other_label: str
+) -> list[dict[str, Any]]:
+    """
+    Top-N 标签计数 + 一条「其他」汇总剩余行数，使 ``count`` 之和等于非空标签行总数
+    （与 §4 集中度表、扇形图分母一致；否则仅 ``most_common(N)`` 会丢掉长尾店铺/品牌行）。
+    """
+    cleaned = [(x or "").strip() for x in labels if (x or "").strip()]
+    if not cleaned:
+        return []
+    cnt = Counter(cleaned)
+    total_rows = len(cleaned)
+    mc = cnt.most_common(top_n)
+    out: list[dict[str, Any]] = [{"label": k, "count": int(v)} for k, v in mc]
+    covered = sum(v for _, v in mc)
+    rest = total_rows - covered
+    if rest > 0:
+        out.append({"label": other_label, "count": int(rest)})
+    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)"
@@ -2013,7 +2034,7 @@ def build_competitor_markdown(
             _embed_chart(
                 run_dir,
                 "chart_brand_rows_pie.png",
-                "品牌列表曝光占比（扇形图，Top 段合并为「其他」；与上表集中度同源）",
+                "品牌列表曝光占比（扇形图；与上表「含品牌字段行数」同源，长尾已并入「其他」尾桶）",
             )
         )
         lines.extend(
@@ -2055,7 +2076,7 @@ def build_competitor_markdown(
             _embed_chart(
                 run_dir,
                 "chart_shop_rows_pie.png",
-                "店铺列表曝光占比（扇形图；与上表同源）",
+                "店铺列表曝光占比（扇形图；与上表「含店铺名的行数」同源，长尾已并入「其他」尾桶）",
             )
         )
         lines.extend(
@@ -2117,7 +2138,7 @@ def build_competitor_markdown(
     if not grouped_matrix:
         lines.append("*无合并表 SKU。*")
         lines.append("")
-    for gname, grows in grouped_matrix:
+    for gi, (gname, grows) in enumerate(grouped_matrix):
         lines.append(f"### {gname}（**{len(grows)}** 款）")
         lines.append("")
         lines.extend(matrix_header)
@@ -2129,6 +2150,15 @@ def build_competitor_markdown(
                 )
             )
         lines.append("")
+        slug_mx = _scenario_group_asset_slug(gname, gi)
+        lines.extend(
+            _embed_chart(
+                run_dir,
+                f"chart_matrix_prices_reviews__{slug_mx}.png",
+                f"「{_md_cell(gname, 24)}」细类 · **展示价与评价量**（条形图；与上表同源："
+                f"价取 detail_price_final→标价→券后 优先可解析数值；评价量为搜索侧「评价量」字段摘录）",
+            )
+        )
 
     _mx_llm = (llm_matrix_section_md or "").strip()
     if _mx_llm:
@@ -2684,18 +2714,16 @@ def build_competitor_brief(
         "category_mix_top": [
             {"label": lbl, "count": cnt} for lbl, cnt in cm_structure
         ],
-        "list_brand_mix_top": [
-            {"label": k, "count": v}
-            for k, v in Counter(
-                b for b in brands_s if (b or "").strip()
-            ).most_common(24)
-        ],
-        "list_shop_mix_top": [
-            {"label": k, "count": v}
-            for k, v in Counter(
-                s for s in shops_s if (s or "").strip()
-            ).most_common(24)
-        ],
+        "list_brand_mix_top": _label_count_dicts_top_n_plus_other(
+            brands_s,
+            top_n=24,
+            other_label="其他（Top24 以外品牌行数合计）",
+        ),
+        "list_shop_mix_top": _label_count_dicts_top_n_plus_other(
+            shops_s,
+            top_n=24,
+            other_label="其他（Top24 以外店铺行数合计）",
+        ),
         "price_stats": pst,
         "price_stats_source": price_stats_source,
         "price_stats_merged_sample": pst_merged,

backend/pipeline/jd_runner.py

@@ -22,6 +22,8 @@ def merge_llm_supplement_with_rules_report(llm_md: str, rules_md: str) -> str:
 
     大模型稿作为 **§8.5** 嵌入在 **第八章末、第九章策略** 之前，与 §8.2～8.4 等具体分析同卷连贯，
     **不再**插在篇首「## 一、」之前。
+
+    注：API「重新生成报告」已不再调用本函数，避免整篇 LLM 与矩阵/图表口径冲突；保留供脚本或将来显式开关复用。
     """
     body = (rules_md or "").strip()
     sup = (llm_md or "").strip()

backend/pipeline/report_charts.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import math
 import os
 import re
 from pathlib import Path
@@ -63,6 +64,36 @@ def _merge_labeled_counts_tail(
     return head
 
 
+def _float_price_from_cell(s: str) -> float | None:
+    t = (s or "").strip().replace(",", "").replace("，", "")
+    if not t:
+        return None
+    m = re.search(r"(\d+(?:\.\d+)?)", t)
+    if not m:
+        return None
+    try:
+        v = float(m.group(1))
+    except ValueError:
+        return None
+    if 0 < v < 1_000_000:
+        return v
+    return None
+
+
+def _reviews_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」）。"""
+    t = (s or "").strip().replace(",", "").replace("，", "")
+    if not t:
+        return 0
+    m = re.search(r"(\d+(?:\.\d+)?)\s*万", t)
+    if m:
+        return int(round(float(m.group(1)) * 10_000))
+    m2 = re.search(r"(\d+)", t)
+    if m2:
+        return int(m2.group(1))
+    return 0
+
+
 def _merge_tail_as_other(
     labels: list[str], values: list[float], *, max_slices: int
 ) -> tuple[list[str], list[float]]:
@@ -377,4 +408,78 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]:
             "条数",
         )
 
+    matrix_groups = brief.get("matrix_by_group") or []
+    if isinstance(matrix_groups, list):
+        for gi, block in enumerate(matrix_groups):
+            if not isinstance(block, dict):
+                continue
+            gname = str(block.get("group") or "").strip()
+            skus = block.get("skus") or []
+            if not isinstance(skus, list) or not skus:
+                continue
+            slug = scenario_group_asset_slug(gname, gi)
+            rows_data: list[tuple[str, float | None, int]] = []
+            for s in skus:
+                if not isinstance(s, dict):
+                    continue
+                sku = str(s.get("sku_id") or "").strip()
+                title = str(s.get("title") or "").strip()
+                if sku:
+                    label = sku if len(sku) <= 20 else sku[:18] + "…"
+                else:
+                    label = title if len(title) <= 16 else title[:14] + "…"
+                if not label:
+                    label = "?"
+                p: float | None = None
+                for k in (
+                    "detail_price_final",
+                    "list_price_show",
+                    "coupon_or_detail_price",
+                ):
+                    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 ""))
+                rows_data.append((label, p, rev))
+            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
+                for _, pr, rv 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]
+            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(
+                1, 2, figsize=(10.6, fig_h), sharey=True
+            )
+            for yi, pr in enumerate(prices_mx):
+                if pr is not None and pr > 0 and math.isfinite(pr):
+                    ax_l.barh(yi, pr, height=0.62, color="#2563eb")
+            ax_l.set_yticks(y_pos)
+            ax_l.set_yticklabels(labels_mx, fontsize=8)
+            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.set_xlabel("评价量（搜索侧）", fontsize=9)
+            ax_r.set_title("评价量 / 声量", fontsize=10, pad=8)
+            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）",
+                fontsize=11,
+                y=1.01,
+            )
+            fig.tight_layout()
+            out_mx = out_dir / f"chart_matrix_prices_reviews__{slug}.png"
+            fig.savefig(out_mx, dpi=130, bbox_inches="tight")
+            plt.close(fig)
+            created.append(out_mx.name)
+
     return created

backend/pipeline/views.py

@@ -36,14 +36,9 @@ from .ingest import ingest_job_full
 from .jd_runner import (
     build_competitor_brief_for_job,
     get_default_report_config,
-    merge_llm_supplement_with_rules_report,
     regenerate_competitor_report,
-    write_competitor_analysis_markdown,
-)
-from .llm_generate import (
-    generate_competitor_report_markdown_llm,
-    generate_strategy_draft_markdown_llm,
 )
+from .llm_generate import generate_strategy_draft_markdown_llm
 from .md_document_export import markdown_to_docx_bytes, markdown_to_pdf_bytes
 from .models import (
     JdJobCommentRow,
@@ -344,48 +339,11 @@ class JobRegenerateReportView(APIView):
         except ValueError as e:
             return Response({"detail": str(e)}, status=status.HTTP_400_BAD_REQUEST)
         if generator == "llm":
-            try:
-                rules_md = (
-                    Path(job.run_dir) / "competitor_analysis.md"
-                ).read_text(encoding="utf-8")
-                brief = build_competitor_brief_for_job(
-                    job.run_dir, job.keyword, report_config=rc
-                )
-                md = generate_competitor_report_markdown_llm(brief, job.keyword)
-                md = merge_llm_supplement_with_rules_report(md, rules_md)
-                write_competitor_analysis_markdown(job.run_dir, md)
-            except FileNotFoundError as e:
-                return Response({"detail": str(e)}, status=status.HTTP_400_BAD_REQUEST)
-            except ValueError as e:
-                msg = str(e)
-                logger.warning(
-                    "regenerate-report LLM ValueError job_id=%s: %s", pk, msg
-                )
-                # AI_crawler：缺密钥/网关、提示词过长；jd_runner：run_dir 越界等
-                if "run_dir 不在京东数据目录下" in msg:
-                    return Response(
-                        {"detail": msg},
-                        status=status.HTTP_400_BAD_REQUEST,
-                    )
-                if "请设置环境变量" in msg:
-                    return Response(
-                        {"detail": msg + "（运行 Django 的终端需能读取到该环境变量）"},
-                        status=status.HTTP_400_BAD_REQUEST,
-                    )
-                if "提示词过长" in msg or "上下文上限" in msg:
-                    return Response(
-                        {"detail": msg},
-                        status=status.HTTP_400_BAD_REQUEST,
-                    )
-                return Response(
-                    {"detail": msg},
-                    status=status.HTTP_503_SERVICE_UNAVAILABLE,
-                )
-            except requests.RequestException as e:
-                return Response(
-                    {"detail": f"大模型网关错误：{e}"},
-                    status=status.HTTP_502_BAD_GATEWAY,
-                )
+            logger.info(
+                "regenerate-report job_id=%s: generator=llm 已忽略；"
+                "规则正文（矩阵与统计图）为唯一输出，不再并入整篇大模型 §8.5。",
+                pk,
+            )
         return Response(PipelineJobSerializer(job).data)