diff --git a/AlgorithmModule/src/ImgCheckAnalysisy.cpp b/AlgorithmModule/src/ImgCheckAnalysisy.cpp
index 8af85b4..da85e02 100644
--- a/AlgorithmModule/src/ImgCheckAnalysisy.cpp
+++ b/AlgorithmModule/src/ImgCheckAnalysisy.cpp
@@ -476,6 +476,38 @@ int ImgCheckAnalysisy::CheckRun()
     float diff_align = std::fabs(aouter - ainner);
     diff_align = std::fmod(diff_align, 180.0f);  // 模 180 度
 
+	// 两条直线的夹角取锐角：min(diff, 180 - diff)
+	if (diff_align > 90.0f)
+	{
+		diff_align = 180.0f - diff_align;
+	}
+
+	// 如果diff_align大于10，则添加到缺陷列表
+	if (diff_align > 10.0f)
+	{
+		QX_ERROR_INFO_ alignErr;
+		alignErr.Idx = static_cast<int>(m_pDetResult->pQx_ErrorList->size());
+
+		// roi 用整张检测图区域（detImg 坐标系）
+		cv::Rect tplRect = m_tplOuterRect.boundingRect();
+		alignErr.roi = cv::Rect(0, 0, tplRect.width, tplRect.height);
+		alignErr.area = alignErr.roi.width * alignErr.roi.height;
+		alignErr.JudgArea = alignErr.area * m_fImgage_Scale_X * m_fImgage_Scale_Y;
+		alignErr.flen = tplRect.width * m_fImgage_Scale_X;
+		alignErr.fbreadth = tplRect.height * m_fImgage_Scale_Y;
+
+		alignErr.nconfig_qx_type = CONFIG_QX_NAME_cell_zangwu;
+		alignErr.qx_name = CONFIG_QX_NAME_Names[CONFIG_QX_NAME_cell_zangwu];
+		alignErr.grayDis = diff_align; // 记录内外边缘夹角（度）
+		alignErr.detRegionidxList.push_back(0);
+
+		alignErr.detlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Align Check",
+									 "outer-inner angle diff %.2f deg", diff_align);
+		m_pDetResult->pQx_ErrorList->push_back(alignErr);
+
+		m_pdetlog->AddCheckstr(PrintLevel_0, "Align Check", "angle diff %.2f deg > 10", diff_align);
+	}
+
 	/*投影对齐模板*/
 	Adapt_Config(m_tplOuterRect, m_outer_rroi, m_CheckResult_shareP->in_shareImage->img);
 	m_outer_roi = m_tplOuterRect.boundingRect();
@@ -1854,6 +1886,7 @@ int ImgCheckAnalysisy::Pin_Qx_Det(const cv::Mat &img)
 	const float pin_scale_x = (pin_mask.cols > 0) ? static_cast<float>(img.cols) / pin_mask.cols : 0.0f;
 	const float pin_scale_y = (pin_mask.rows > 0) ? static_cast<float>(img.rows) / pin_mask.rows : 0.0f;
 	// 将轮廓点从 mask 坐标系 映射回 tplImg 坐标系
+	m_curPinContours.clear();
 	m_curPinContours.reserve(pin_contours.size());
 	for (size_t i = 0; i < pin_contours.size(); i++)
 	{
@@ -1867,6 +1900,123 @@ int ImgCheckAnalysisy::Pin_Qx_Det(const cv::Mat &img)
 		m_curPinContours.push_back(mapped);
 	}
 
+	// 根据模板Pin和当前Pin轮廓进行对比，判断当前有无缺失和偏移
+	{
+		// 计算轮廓中心点（外接矩形中心）
+		auto getCenter = [](const std::vector<cv::Point> &contour) -> cv::Point2f
+		{
+			cv::Rect r = cv::boundingRect(contour);
+			return cv::Point2f(r.x + r.width * 0.5f, r.y + r.height * 0.5f);
+		};
+
+		const int nTpl = static_cast<int>(m_tplPinContours.size());
+		const int nCur = static_cast<int>(m_curPinContours.size());
+
+		if (nTpl > 0)
+		{
+			// 1、统计模板 pin 中心与平均最小边长，用于自适应阈值
+			std::vector<cv::Point2f> tplCenters(nTpl);
+			double sumMinDim = 0.0;
+			for (int i = 0; i < nTpl; i++)
+			{
+				tplCenters[i] = getCenter(m_tplPinContours[i]);
+				cv::Rect r = cv::boundingRect(m_tplPinContours[i]);
+				sumMinDim += (r.width < r.height) ? r.width : r.height;
+			}
+			const float avgMinDim = static_cast<float>(sumMinDim / nTpl);
+
+			// 偏移阈值约 1/4 个 pin 尺寸，缺失阈值约 3/4 个 pin 尺寸
+			float offsetTh = avgMinDim * 0.5f;
+			if (offsetTh < 3.0f)
+			{
+				offsetTh = 3.0f;
+			}
+			float missTh = avgMinDim * 0.75f;
+			if (missTh < offsetTh + 1.0f)
+			{
+				missTh = offsetTh + 1.0f;
+			}
+
+			// 2、当前 pin 中心
+			std::vector<cv::Point2f> curCenters(nCur);
+			for (int i = 0; i < nCur; i++)
+			{
+				curCenters[i] = getCenter(m_curPinContours[i]);
+			}
+			std::vector<bool> curUsed(nCur, false);
+
+			// 上报缺失/偏移缺陷（缺陷类型可按需调整）
+			auto reportPinError = [&](int pinIdx, const cv::Point2f &tplCenter, const std::string &reason)
+			{
+				QX_ERROR_INFO_ pinErr;
+				pinErr.Idx = static_cast<int>(m_pDetResult->pQx_ErrorList->size());
+				cv::Rect r = cv::boundingRect(m_tplPinContours[pinIdx]);
+				pinErr.roi = r;
+				pinErr.area = static_cast<int>(cv::contourArea(m_tplPinContours[pinIdx]));
+				pinErr.JudgArea = pinErr.area * m_fImgage_Scale_X * m_fImgage_Scale_Y;
+				int longSide = (r.width > r.height) ? r.width : r.height;
+				int shortSide = (r.width < r.height) ? r.width : r.height;
+				pinErr.flen = longSide * m_fImgage_Scale_X;
+				pinErr.fbreadth = shortSide * m_fImgage_Scale_Y;
+				pinErr.nconfig_qx_type = CONFIG_QX_NAME_cell_ymhs;
+				pinErr.qx_name = CONFIG_QX_NAME_Names[CONFIG_QX_NAME_cell_ymhs];
+				pinErr.detRegionidxList.push_back(0);
+				pinErr.detlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Pin Check",
+										   "pin %d  center(%d,%d)  %s",
+										   pinIdx, static_cast<int>(tplCenter.x), static_cast<int>(tplCenter.y),
+										   reason.c_str());
+				m_pDetResult->pQx_ErrorList->push_back(pinErr);
+			};
+
+			int nMiss = 0;
+			int nOffset = 0;
+
+			// 3、逐个模板 pin 找最近且未被占用的当前 pin
+			for (int i = 0; i < nTpl; i++)
+			{
+				int bestIdx = -1;
+				double bestDist = 1e12;
+				for (int j = 0; j < nCur; j++)
+				{
+					if (curUsed[j])
+					{
+						continue;
+					}
+					float dx = tplCenters[i].x - curCenters[j].x;
+					float dy = tplCenters[i].y - curCenters[j].y;
+					double d = std::sqrt(static_cast<double>(dx) * dx + static_cast<double>(dy) * dy);
+					if (d < bestDist)
+					{
+						bestDist = d;
+						bestIdx = j;
+					}
+				}
+
+				// 无候选或距离过远 -> 缺失
+				if (bestIdx < 0 || bestDist > missTh)
+				{
+					nMiss++;
+					reportPinError(i, tplCenters[i], "miss");
+					m_pdetlog->AddCheckstr(PrintLevel_0, "Pin Check", "pin %d MISS", i);
+					continue;
+				}
+
+				curUsed[bestIdx] = true;
+
+				// 距离超出偏移阈值 -> 偏移
+				if (bestDist > offsetTh)
+				{
+					nOffset++;
+					char buf[64];
+					snprintf(buf, sizeof(buf), "offset %.1fpx", bestDist);
+					reportPinError(i, tplCenters[i], buf);
+					m_pdetlog->AddCheckstr(PrintLevel_0, "Pin Check", "pin %d OFFSET %.1fpx", i, bestDist);
+				}
+			}
+
+			m_pdetlog->AddCheckstr(PrintLevel_0, "Pin Check", "tpl %d  cur %d  miss %d  offset %d", nTpl, nCur, nMiss, nOffset);
+		}
+	}
     
     if (m_Edge_DetConfig.bSaveResultImg)
     {