fix: stabilize dense-text rejection and serialize delayed image flush
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@@ -468,7 +468,12 @@ class QingjianAPIClient:
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"""判断文本是否指代之前的图片(图一/图二/这张/那张/上面那张等)"""
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if not msg:
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return False
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refs = ("图一", "图二", "第一张", "第二张", "这张", "那张", "上面那张", "下面那张", "刚才那张", "上一张", "下一张")
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refs = (
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"图一", "图二", "第一张", "第二张",
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"这张", "那张", "这图", "那个图",
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"这个", "这个呢",
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"上面那张", "下面那张", "刚才那张", "上一张", "下一张",
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)
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return any(r in msg for r in refs)
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def _extract_image_urls(self, msg: str) -> list:
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@@ -612,6 +617,8 @@ class QingjianAPIClient:
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old.cancel()
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async def _delay_flush(capture_key, capture_data):
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await asyncio.sleep(self._DEBOUNCE_SECONDS + 4)
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# 与同客户 agent_reply 串行,避免“延迟报价”和“当前追问”并发打架
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async with self._get_customer_lock(capture_key):
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await self._flush_pending_images(capture_key, capture_data)
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task = asyncio.create_task(_delay_flush(key, data))
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self._pending_image_tasks[key] = task
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@@ -241,6 +241,7 @@ class ImageAnalyzer:
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DENSE_TEXT_SUBJECT_KEYWORDS = (
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"宣传栏", "公告栏", "展板", "海报墙", "通知栏", "知识栏", "制度牌", "公示栏", "墙报", "密密麻麻",
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"宣传海报", "知识海报", "科普海报", "防灾减灾", "宣传板", "宣传页",
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"表格", "检索表", "配伍表", "药物配伍", "课程表", "流程表", "说明表", "数据表",
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"word wall", "poster wall", "bulletin board",
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)
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@@ -571,6 +572,10 @@ class ImageAnalyzer:
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kw in scene_text
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for kw in self.DENSE_TEXT_SUBJECT_KEYWORDS
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)
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dense_text_hint = any(
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kw in scene_text
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for kw in ("密集文字", "大量文字", "小字", "多板块", "细字")
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)
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# 校验比例合法性
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valid_ratios = {"1:1", "9:16", "16:9", "3:4", "4:3", "3:2", "2:3", "5:4", "4:5"}
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@@ -640,7 +645,8 @@ class ImageAnalyzer:
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# 硬规则1:文字很多(>100)且密密麻麻不接单
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text_gt_100 = text_amount in ["大量 (51-200 字)", "极多 (200 字以上)"]
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if text_gt_100 or dense_text_scene:
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dense_text_hard_reject = text_gt_100 or dense_text_scene or (has_text == "yes" and dense_text_hint)
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if dense_text_hard_reject:
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feasibility = "no"
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risk = "high"
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note = "文字内容过于密集(如宣传栏/公告栏),暂不接单处理"
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