diff --git a/AlgorithmModule/include/ImageDetConfig.h b/AlgorithmModule/include/ImageDetConfig.h
index 24df4f1..1f9a5df 100644
--- a/AlgorithmModule/include/ImageDetConfig.h
+++ b/AlgorithmModule/include/ImageDetConfig.h
@@ -98,6 +98,18 @@ struct One_Image_CheckResult_
 	cv::Rect CutRoi;											// 当前检测图片 对应的裁切区域
 	cv::Rect Param_CropRoi;										// 参数模版     对应的裁切区域
 	std::shared_ptr<std::vector<QX_ERROR_INFO_>> pQx_ErrorList; // 缺陷错误信息
+
+	bool bBlurNG;    // 图像模糊判定: true = 检测图像模糊(判 NG)
+	float fBlurScore; // 图像清晰度(拉普拉斯方差), 越大越清晰
+
+	One_Image_CheckResult_()
+	{
+		CutRoi = cv::Rect(0, 0, 0, 0);
+		Param_CropRoi = cv::Rect(0, 0, 0, 0);
+		pQx_ErrorList = std::make_shared<std::vector<QX_ERROR_INFO_>>();
+		bBlurNG = false;
+		fBlurScore = 0.0f;
+	}
 	void print(std::string str = "")
 	{
 		std::cout << str << ": "
diff --git a/AlgorithmModule/include/ImgCheckAnalysisy.hpp b/AlgorithmModule/include/ImgCheckAnalysisy.hpp
index f6e6e0d..b42e8f7 100644
--- a/AlgorithmModule/include/ImgCheckAnalysisy.hpp
+++ b/AlgorithmModule/include/ImgCheckAnalysisy.hpp
@@ -125,6 +125,9 @@ private:
     // 图片预处理
     int ImgPreDet();
 
+    // 图像模糊度(清晰度)检测: 在 AI 检测之前执行, 返回 1 表示图像模糊(判 NG), 0 正常
+    int BlurCheck(const cv::Mat &img);
+
     // 设置新的检测参数
     int SetNewConfig();
 
diff --git a/AlgorithmModule/src/ALLImgCheckAnalysisy.cpp b/AlgorithmModule/src/ALLImgCheckAnalysisy.cpp
index c91c9d6..8b0dfe4 100644
--- a/AlgorithmModule/src/ALLImgCheckAnalysisy.cpp
+++ b/AlgorithmModule/src/ALLImgCheckAnalysisy.cpp
@@ -623,7 +623,8 @@ int ALLImgCheckAnalysisy::SetProductResult(std::shared_ptr<Product> product)
 
 			std::shared_ptr<CheckResult> pCheckResult = pcam->ImageALLDetResultList.at(i)->result;
 
-			if (pCheckResult->qxImageResult.size() > 0)
+			// 图像 NG(包括图像模糊在 AI 检测之前直接判定的 NG) 也要计入产品级结果
+			if (pCheckResult->qxImageResult.size() > 0 || pCheckResult->nresult > ERROR_TYPE_OK)
 			{
 				bNG = true;
 			}
diff --git a/AlgorithmModule/src/CheckResultJson.cpp b/AlgorithmModule/src/CheckResultJson.cpp
index fd34292..55f029f 100644
--- a/AlgorithmModule/src/CheckResultJson.cpp
+++ b/AlgorithmModule/src/CheckResultJson.cpp
@@ -18,6 +18,11 @@ Json::Value CheckResultJson::toJsonValue()
 		root["Param_CropRoi"]["width"] = m_pOneImgDetResult->Param_CropRoi.width;
 		root["Param_CropRoi"]["height"] = m_pOneImgDetResult->Param_CropRoi.height;
 	}
+	{
+		// 图像模糊度(清晰度)检测结果: 在 AI 检测之前计算
+		root["blurScore"] = m_pOneImgDetResult->fBlurScore;
+		root["isBlur"] = m_pOneImgDetResult->bBlurNG;
+	}
 	for (int i = 0; i < m_pOneImgDetResult->pQx_ErrorList->size(); i++)
 	{
 		QX_ERROR_INFO_ *p = &m_pOneImgDetResult->pQx_ErrorList->at(i);
@@ -86,6 +91,17 @@ void CheckResultJson::toObjectFromValue(Json::Value root)
 			m_pOneImgDetResult->Param_CropRoi.height = root["Param_CropRoi"]["height"].asInt();
 		}
 	}
+	{
+		// 图像模糊度(清晰度)检测结果; 旧版本 json 可能没有该字段
+		if (root.isMember("blurScore"))
+		{
+			m_pOneImgDetResult->fBlurScore = root["blurScore"].asFloat();
+		}
+		if (root.isMember("isBlur"))
+		{
+			m_pOneImgDetResult->bBlurNG = root["isBlur"].asBool();
+		}
+	}
 	{
 		if (root.isMember("Qx"))
 		{
diff --git a/AlgorithmModule/src/ImageResultJudge.cpp b/AlgorithmModule/src/ImageResultJudge.cpp
index 5820116..30e3371 100644
--- a/AlgorithmModule/src/ImageResultJudge.cpp
+++ b/AlgorithmModule/src/ImageResultJudge.cpp
@@ -147,6 +147,14 @@ int ImageResultJudge::ResultJudge(std::shared_ptr<ImageAllResult> pImageResult)
 	std::shared_ptr<CheckResult> m_CheckResult_shareP = pImageResult->result;	   // 返回结果
 	int qxNum = pDetResult->pQx_ErrorList->size();
 
+	// 图像模糊: 检测阶段已在 AI 检测之前判定, 这里保证结果保持 NG(复测 ReJson 读取 json 后同样生效)
+	if (pDetResult->bBlurNG)
+	{
+		m_CheckResult_shareP->nresult = 1;
+		pImageLog->AddCheckstr(PrintLevel_0, "Blur Check",
+							   "image blurred -> NG (variance %.2f)", pDetResult->fBlurScore);
+	}
+
 	// map<string, vector<iregionQxZoningInfo>> allQxZoning;
 
 	map<string, vector<singleQxZoningInfo>> allQxZoning;
diff --git a/AlgorithmModule/src/ImgCheckAnalysisy.cpp b/AlgorithmModule/src/ImgCheckAnalysisy.cpp
index 80414d1..d7f5d58 100644
--- a/AlgorithmModule/src/ImgCheckAnalysisy.cpp
+++ b/AlgorithmModule/src/ImgCheckAnalysisy.cpp
@@ -23,6 +23,11 @@ static const float  PIN_SHIFT_BIN_SIZE_PX       = 4.0f; // 位移投票的网格
 static const float  PIN_SHIFT_MAX_GAP_RATIO     = 0.8f; // 整体位移量级上限 = 相邻pin最小间距 * 该比例
 // ======================================================================================
 
+// ===================== 图像模糊度(清晰度)检测参数 =====================
+// 计算清晰度前把检测图最长边缩放到该尺寸(只缩小不放大), 使阈值与图像分辨率无关
+static const int BLUR_CHECK_MAX_SIZE = 1000;
+// ======================================================================
+
 // 计算点相对旋转矩形“长边轴线”的带符号法向距离(检测图坐标系):
 // 取矩形长边方向作为 Pin 排方向, 返回点沿短边(法线)方向相对矩形中心的投影;
 // 正负号天然把分布在矩形两侧的 Pin 分成两组(上下两排分别估计整体偏移)。
@@ -804,6 +809,98 @@ int ImgCheckAnalysisy::TransformTplPinContoursToCur()
 	return 0;
 }
 
+// 图像模糊度(清晰度)检测: 必须在 AI 检测之前执行。
+//   1) 检测图 detImg 转灰度(本身就是单通道, 这里做兼容);
+//   2) 最长边缩放到 BLUR_CHECK_MAX_SIZE(只缩小不放大), 保证阈值与图像分辨率无关;
+//   3) 用拉普拉斯算子(CV_64F)响应的方差作为清晰度指标: 方差越小越模糊;
+//   4) 方差 < 阈值(基础参数 blurDet.fThreshold, 对应界面参数 blur_threshold) -> 判为模糊图片,
+//      参数 blurDet.bNG(界面 bNG)打开时直接判 NG。
+// 返回: 1 图像模糊且需要判 NG; 0 正常 / 功能未开启 / 图像无效
+int ImgCheckAnalysisy::BlurCheck(const cv::Mat &img)
+{
+	if (m_pbaseCheckFunction == NULL || !m_pbaseCheckFunction->blurDet.bOpen ||
+		m_pbaseCheckFunction->blurDet.fThreshold <= 0.0f)
+	{
+		return 0;
+	}
+
+	// 每帧重置模糊判定结果
+	if (m_pDetResult)
+	{
+		m_pDetResult->bBlurNG = false;
+		m_pDetResult->fBlurScore = 0.0f;
+	}
+
+	if (img.empty() || img.cols < 5 || img.rows < 5)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Blur Check", "input img invalid, skip");
+		return 0;
+	}
+
+	// 1、转灰度(detImg 为单通道, 其他通道情况做兼容)
+	cv::Mat grayImg;
+	if (1 == img.channels())
+	{
+		grayImg = img;
+	}
+	else if (3 == img.channels())
+	{
+		cv::cvtColor(img, grayImg, cv::COLOR_BGR2GRAY);
+	}
+	else if (4 == img.channels())
+	{
+		cv::cvtColor(img, grayImg, cv::COLOR_BGRA2GRAY);
+	}
+	else
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "Blur Check", "unsupported channels %d, skip", img.channels());
+		return 0;
+	}
+
+	// 2、按最长边缩放(只缩小不放大)
+	cv::Mat checkImg = grayImg;
+	const int nMaxSide = std::max(grayImg.cols, grayImg.rows);
+	if (nMaxSide > BLUR_CHECK_MAX_SIZE)
+	{
+		const double fScale = static_cast<double>(BLUR_CHECK_MAX_SIZE) / static_cast<double>(nMaxSide);
+		cv::resize(grayImg, checkImg, cv::Size(), fScale, fScale, cv::INTER_AREA);
+	}
+
+	// 3、拉普拉斯方差(越大越清晰)
+	cv::Mat lapImg;
+	cv::Laplacian(checkImg, lapImg, CV_64F, 3);
+	cv::Scalar meanVal;
+	cv::Scalar stdVal;
+	cv::meanStdDev(lapImg, meanVal, stdVal);
+	const float fScore = static_cast<float>(stdVal[0] * stdVal[0]);
+	const float fThreshold = m_pbaseCheckFunction->blurDet.fThreshold;
+	const bool bNGOpen = m_pbaseCheckFunction->blurDet.bNG;
+	const bool bBlur = (fScore < fThreshold);
+
+	if (m_pDetResult)
+	{
+		m_pDetResult->fBlurScore = fScore;
+		m_pDetResult->bBlurNG = (bBlur && bNGOpen);
+	}
+
+	m_pdetlog->AddCheckstr(PrintLevel_0, "Blur Check",
+						   "variance %.2f %s threshold %.2f -> %s (bNG %d, img [%d %d] -> check [%d %d])",
+						   fScore, bBlur ? "<" : ">=", fThreshold, bBlur ? "BLUR" : "OK",
+						   static_cast<int>(bNGOpen),
+						   grayImg.cols, grayImg.rows, checkImg.cols, checkImg.rows);
+
+	if (bBlur && bNGOpen)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_0, "Blur Check", "image blurred -> judge NG");
+		return 1;
+	}
+	if (bBlur)
+	{
+		m_pdetlog->AddCheckstr(PrintLevel_0, "Blur Check", "image blurred, but bNG is off -> not NG");
+	}
+	return 0;
+}
+
 int ImgCheckAnalysisy::CheckRun()
 {
 
@@ -1064,6 +1161,15 @@ int ImgCheckAnalysisy::CheckRun()
 	{
 		m_pImageAllResult->detImg = image(m_outer_roi).clone();
 	}
+
+	/*图像模糊度(清晰度)检测: 必须在 AI 检测之前完成, 模糊图片直接判 NG*/
+	long time_blur_s = CheckUtil::getcurTime();
+	const bool bBlurNG = (BlurCheck(m_pImageAllResult->detImg) == 1);
+	long time_blur_e = CheckUtil::getcurTime();
+	m_pdetlog->AddCheckstr(PrintLevel_0, "2.2、pre detect",
+						   "-------------------------Blur Check------------%s---time %ld----\n",
+						   bBlurNG ? "NG" : "OK", time_blur_e - time_blur_s);
+
 	/*多线程开启 AI 推理检测*/
 	long time_AI_s = CheckUtil::getcurTime();
 	m_AItask = std::make_shared<TaskInfo>();
@@ -1086,6 +1192,12 @@ int ImgCheckAnalysisy::CheckRun()
 
 	m_CheckResult_shareP->nresult = 0;
 
+	// 图像模糊: 在 AI 检测之前已判定, 直接判 NG(nresult=1 与区域参数判断出的 NG 语义一致)
+	if (bBlurNG)
+	{
+		m_CheckResult_shareP->nresult = 1;
+	}
+
 	/*把临时可以绘制的结果都绘制出来。*/
 	DrawResult_Step_1();
 	long time_AI_e;
diff --git a/ConfigModule/include/CheckConfigDefine.h b/ConfigModule/include/CheckConfigDefine.h
index 94dbacf..d5f6f2c 100644
--- a/ConfigModule/include/CheckConfigDefine.h
+++ b/ConfigModule/include/CheckConfigDefine.h
@@ -1187,6 +1187,46 @@ struct Base_Function_Merge_QueXi
 
 };
 
+// 基础检测功能  图像模糊度(清晰度)检测
+// 执行时机: AI 残点检测之前, 对送检图(检测图 detImg)计算模糊度, 模糊图片可直接判 NG
+// 界面参数: itemCode = "Blur_Detect", form.Blur_Config = { bNG, blur_threshold }
+struct Base_Function_Blur
+{
+
+	bool bOpen;        // 是否开启
+	bool bNG;          // 模糊图片是否直接判 NG
+	float fThreshold;  // 模糊度阈值(拉普拉斯方差下限): 方差低于该值判为模糊
+
+	Base_Function_Blur()
+	{
+		Init();
+	}
+	void Init()
+	{
+		bOpen = true;        // 默认开启(参数里没有该配置项时也做模糊判定)
+		bNG = true;          // 默认模糊即 NG
+		fThreshold = 800.0f; // 默认阈值(实测: 清晰图 ~8100, sigma=3 模糊 ~880, sigma=5 模糊 ~250)
+	}
+
+	void copy(Base_Function_Blur tem)
+	{
+		this->bOpen = tem.bOpen;
+		this->bNG = tem.bNG;
+		this->fThreshold = tem.fThreshold;
+	}
+	void print(std::string str)
+	{
+		printf("%s>>bOpen %d bNG %d fThreshold %f \n", str.c_str(), bOpen, bNG, fThreshold);
+	}
+	std::string GetInfo(std::string str)
+	{
+		char buffer[256];
+		sprintf(buffer, "%s>>bOpen %d bNG %d fThreshold %f \n", str.c_str(), bOpen, bNG, fThreshold);
+		std::string str123 = buffer;
+		return str123;
+	}
+};
+
 // 基础检测功能 大缺陷 NG
 struct Base_Function_BigNG
 {
@@ -1295,6 +1335,7 @@ struct BaseCheckFunction
 	Base_Function_Pin_Det pinDet;
 	Base_Function_SaveImg saveImg;
 	Base_Function_Merge_QueXi Merge_QueXi;
+	Base_Function_Blur blurDet;
 	Base_Function_BigNG bigNG;
 	BaseCheckFunction()
 	{
@@ -1306,6 +1347,7 @@ struct BaseCheckFunction
 		pinDet.Init();
 		saveImg.Init();
 		Merge_QueXi.Init();
+		blurDet.Init();
 		bigNG.Init();
 	}
 	void copy(BaseCheckFunction tem)
@@ -1314,6 +1356,7 @@ struct BaseCheckFunction
 		this->pinDet.copy(tem.pinDet);
 		this->saveImg.copy(tem.saveImg);
 		this->Merge_QueXi.copy(tem.Merge_QueXi);
+		this->blurDet.copy(tem.blurDet);
 		this->bigNG.copy(tem.bigNG);
 	}
 	void print(std::string str)
@@ -1323,6 +1366,7 @@ struct BaseCheckFunction
 		pinDet.print("pinDet");
 		saveImg.print("saveImg");
 		Merge_QueXi.print("Merge_QueXi");
+		blurDet.print("blurDet");
 		bigNG.print("bigNG");
 	}
 	std::string GetInfo(std::string str)
@@ -1332,6 +1376,7 @@ struct BaseCheckFunction
 		str123 += pinDet.GetInfo("pinDet");
 		str123 += saveImg.GetInfo("saveImg");
 		str123 += Merge_QueXi.GetInfo("Merge_QueXi");
+		str123 += blurDet.GetInfo("blurDet");
 		str123 += bigNG.GetInfo("bigNG");
 		// str123 += "\n";
 		return str123;
diff --git a/ConfigModule/src/JsonConfig.cpp b/ConfigModule/src/JsonConfig.cpp
index fcd9905..91f3e7f 100644
--- a/ConfigModule/src/JsonConfig.cpp
+++ b/ConfigModule/src/JsonConfig.cpp
@@ -887,6 +887,43 @@ int BaseFuntonConfigJson::GetFunction(Json::Value value)
         _config.Merge_QueXi.bOpen = value_f["isOpen"].asBool();
     }
 
+    // 图像模糊度(清晰度)检测: AI 检测之前执行, 模糊图片可判 NG
+    if ("Blur_Detect" == strCode)
+    {
+        auto value_f = value;
+        // std::cout << value_f << std::endl;
+        // getchar();
+        _config.blurDet.bOpen = value_f["isOpen"].asBool();
+        if (_config.blurDet.bOpen)
+        {
+            if (value_f["form"]["Blur_Config"]["bNG"])
+            {
+                _config.blurDet.bNG = value_f["form"]["Blur_Config"]["bNG"].asBool();
+            }
+            // 模糊度阈值: blur_threshold(界面) / threshold 兼容; 未配置或 <= 0 时保留默认阈值
+            float fThreshold = 0.0f;
+            if (value_f["form"]["Blur_Config"]["blur_threshold"])
+            {
+                fThreshold = value_f["form"]["Blur_Config"]["blur_threshold"].asFloat();
+            }
+            else if (value_f["form"]["Blur_Config"]["threshold"])
+            {
+                fThreshold = value_f["form"]["Blur_Config"]["threshold"].asFloat();
+            }
+            if (fThreshold > 0.0f)
+            {
+                _config.blurDet.fThreshold = fThreshold;
+            }
+        }
+        else
+        {
+            // 关闭: 阈值置 0, 不做模糊判定
+            _config.blurDet.bOpen = false;
+            _config.blurDet.fThreshold = 0.0f;
+        }
+        // _config.blurDet.print("blurDet");
+    }
+
     if ("Big_NG" == strCode)
     {
         auto value_f = value;