diff --git a/server/README.md b/server/README.md
index 3368444..feaf004 100644
--- a/server/README.md
+++ b/server/README.md
@@ -14,11 +14,11 @@
 | 套餐 | `config.yml` `plans` 节点配置三档套餐（改价 = 改配置重启），价格**整数分**（1000 / 5600 / 18000） |
 | 后台管理端 | `server_admin/`（Vue3 + Element Plus）管理页面：订单查询、账号/授权管理（手动授权/撤销）、App 版本管理；构建产物由后端 `/admin/` 托管，登录页输入 token 后以 `X-Admin-Token` 头鉴权（`config.yml admin.token`） |
 | 版本管理 | 后台管理端上传 Android APK + 更新说明，APK 存服务器 `app.apkDir`（默认 `./workspace/`，与 `./data` 平级、挂载持久化）**固定文件名 `observer-latest.apk`，上传即覆盖，目录永远只保留最新一个文件**；**版本号从文件名识别**：文件须命名为 `observer-x.y.z.apk`（Flutter 打包产物即此命名，版本号取自 pubspec）；客户端启动时 `GET /api/v1/app/update` 检查更新：服务器版本高于本地版本即弹更新提示（不可跳过）。**仅 Android 检查，iOS 不做版本下发**（iOS 走 App Store 自行更新）。版本记录可删除：删最新版本联动删除 APK 文件，删历史版本仅删记录 |
-| 数据训练（唯一入口） | 后台管理端「数据训练」一个菜单承载数据集全流程：**数据集卡片列表**（封面图/描述/图片数/已标注数/**训练状态徽标**），**卡片下方直接展示训练任务进度条与状态**（无独立训练页）；详情页为**图片与标注一体视图**：分页（每页 20 条）逐行「原图 ‖ 标注图」对照展示；**图片入库（手动上传/AI 生成）自动触发 RF-DETR 全图扫描标注**，进度条展示在页顶；页顶另有「全量标注」按钮可手动重标全部图片（覆盖各图已有标注）；点击原图/标注图弹窗放大，弹窗内 canvas 直接画框/点框删除/清空并保存——AI 自动标注结果直接作为标注，人工可修改/清理全部框；封面（上传自动转 jpg + UUID 命名）/**描述**/AI 生成图片（provider 抽象：dashscope 通义万相付费 API / localai 训练机 local-ai qwen-image，`config.yml imageGen` 节点切换，见配置说明）；AI 标注端点与训练机 SSH 为**全局配置，直接读 `config.yml`**（`localAi` / `training.ssh` 节点，改配置需重启服务）；图片落服务器 `app.datasetDir`/`datasets/<数据集名>/`，DB 存元数据 + 标注 JSON |
+| 数据训练（唯一入口） | 后台管理端「数据训练」一个菜单承载数据集全流程：**数据集卡片列表**（封面图/描述/图片数/已标注数/**训练状态徽标**），**卡片下方直接展示训练任务进度条与状态**（无独立训练页）；详情页为**图片与标注一体视图**：分页（每页 20 条）逐行「原图 ‖ 标注图」对照展示；**图片入库（手动上传/AI 生成）自动触发 RF-DETR 全图扫描标注**，进度条展示在页顶；页顶另有「全量标注」按钮可手动重标全部图片（覆盖各图已有标注）；点击原图/标注图弹窗放大，弹窗为**审核视图（不做手动画框）**：点击框选中，列表可确认疑似框/删除误检框/清空并保存——AI 自动标注结果直接作为标注，人工仅审核确认；封面（上传自动转 jpg + UUID 命名）/**描述**/AI 生成图片（provider 抽象：dashscope 通义万相付费 API / localai 训练机 local-ai qwen-image，`config.yml imageGen` 节点切换，见配置说明）；AI 标注端点与训练机 SSH 为**全局配置，直接读 `config.yml`**（`localAi` / `training.ssh` 节点，改配置需重启服务）；图片落服务器 `app.datasetDir`/`datasets/<数据集名>/`，DB 存元数据 + 标注 JSON |
 | 模型训练 | 从数据集卡片「开始训练」一键触发（参数 imgsz/epochs/batch/device 默认走 `config.yml` `training` 节点，部署级配置）：进度/日志/指标监控（每 epoch 粒度）、取消；训练通道 `training` 节点可配置 subprocess（与 Go 服务同机直接起 python）/ ssh（异机执行，SSH 凭据取 `config.yml` `training.ssh` 节点），并发度 1（GPU 独占）；训练脚本 `server/training/train_server.py`（随项目迁移，2026-08-26）参数化，产物（best.tflite/best.pt/曲线）拉回服务器；训练收尾自动做 **tflite 产物自检**（输入/输出 shape 校验，原 `inspect_tflite.py` 逻辑内嵌脚本），自检失败任务置失败并带出原因；`dump_graph.py` 留作训练机人工深度调试 |
 | 模型版本与热更新 | **每数据集一个模型**：训练完成后一键「发布」（训练任务操作列）——tflite 落 `workspace/trainings/<数据集名>.tflite` + sha256/指标/类别名入 `model_version`（按数据集独立版本序列 m1.0.0 递增）。管理端**无模型管理界面**（版本记录仅支撑客户端下发）。**App 模型热更新**：`GET /api/v1/app/update` 扩展返回 `models` 目录数组，客户端独立检查，新模型下载校验替换，失败回退旧模型——模型迭代不再重打包 APK |
 | 模型目录与多模型推理 | `GET /api/v1/models`（登录态）返回全部数据集当前生效模型（数据集/版本/类别/大小/sha256/下载地址），下载 URL `/download/trainings/<数据集名>.tflite`；**App 模型管理页**用户自由下载/删除/启用模型，识别时**加载全部已启用模型并行推理 + 跨模型 NMS 合并**（按类别名），内置 assets 模型兜底 |
-| 标注 | **图片入库自动触发**：手动上传/AI 生成成功后，新增图自动调 `config.yml` `localAi` 节点配置的 AI 端点做 RF-DETR 全图扫描（`label_task` 记录进度，页顶进度条展示；**localAi 未配置 → 上传/生成接口直接报错；已有标注任务在跑（忙）→ 不报错**，当前任务成功完成后自动补标未标注图）→ 扫描结果（**重叠去重**：NMS 风格按置信度降序保留，重叠比 > `localAi.overlapThreshold` 默认 0.3 的框剔除——重叠比 = 交叠面积/两框较小面积，RF-DETR 同目标常输出一大一小两框，此判据能命中，同目标只留置信度最高者）**直接写 `dataset_image.labels_json`**（覆盖该图已有标注，即重标语义）；点击弹窗放大后在 canvas 上画框/点框删除/清空/改类别 → 保存即整体覆写 `dataset_image.labels_json`（YOLO 归一化 JSON 数组，AI 与人工框同存，人工可修改/清理）；`POST /admin/label-tasks` 详情页「全量标注」按钮入口（另有自动触发），可发起全量/指定图重标；自动/手动/混合并存，训练前自动整理（prepare_yolo 逻辑在服务端） |
+| 标注 | **图片入库自动触发**：手动上传/AI 生成成功后，新增图自动调 `config.yml` `localAi` 节点配置的 AI 端点做 RF-DETR 全图扫描（`label_task` 记录进度，页顶进度条展示；**localAi 未配置 → 上传/生成接口直接报错；已有标注任务在跑（忙）→ 不报错**，当前任务成功完成后自动补标未标注图）→ 扫描结果（**重叠去重**：NMS 风格按置信度降序保留，重叠比 > `localAi.overlapThreshold` 默认 0.3 的框剔除——重叠比 = 交叠面积/两框较小面积，RF-DETR 同目标常输出一大一小两框，此判据能命中，同目标只留置信度最高者）**直接写 `dataset_image.labels_json`**（覆盖该图已有标注，即重标语义）；点击弹窗放大进入**审核视图（不做手动画框）**：点击框选中，列表确认疑似框/删除误检框/清空 → 保存即整体覆写 `dataset_image.labels_json`（YOLO 归一化 JSON 数组，AI 标注直写、人工仅审核确认）；`POST /admin/label-tasks` 详情页「全量标注」按钮入口（另有自动触发），可发起全量/指定图重标；自动/手动/混合并存，训练前自动整理（prepare_yolo 逻辑在服务端） |
 
 ## 架构与数据流
 
diff --git a/server/biz/service/dataset.go b/server/biz/service/dataset.go
index a379882..8ed6ad2 100644
--- a/server/biz/service/dataset.go
+++ b/server/biz/service/dataset.go
@@ -605,9 +605,11 @@ func (s *datasetService) buildPromptFromTemplate(ctx context.Context, datasetNam
 			species = pool[rand.Intn(len(pool))]
 		}
 	}
-	// 场景/动作按数据集习性取池（鸭水面、雉鸡灌丛……），未配置该数据集回退通用池
+	// 场景/动作/遮挡按数据集习性取池（鸭水面、雉鸡灌丛……），未配置该数据集回退通用池；
+	// 遮挡：身体完全暴露的个体对识别标注训练无意义，每张必带（程度轻~重随机）
 	scene := pickCfgByDataset(ctx, datasetName, "imageGen.sceneByDataset", "imageGen.scenes")
 	action := pickCfgByDataset(ctx, datasetName, "imageGen.actionByDataset", "imageGen.actions")
+	occlusion := pickCfgByDataset(ctx, datasetName, "imageGen.occlusionByDataset", "imageGen.occlusions")
 	light := pickCfg(ctx, "imageGen.lights")
 	// 轮廓色词按物种（白化个体如白雉鸡配浅色，默认深色剪影）
 	tone := g.Cfg().MustGet(ctx, "imageGen.speciesTone."+species, "深色").String()
@@ -623,8 +625,8 @@ func (s *datasetService) buildPromptFromTemplate(ctx context.Context, datasetNam
 	out := tpl
 	for k, v := range map[string]string{
 		"{scene}": scene, "{species}": species, "{count}": countWord,
-		"{action}": action, "{light}": light, "{distanceWord}": distWord, "{sizeHint}": sizeHint,
-		"{tone}": tone,
+		"{action}": action, "{occlusion}": occlusion, "{light}": light,
+		"{distanceWord}": distWord, "{sizeHint}": sizeHint, "{tone}": tone,
 	} {
 		out = strings.ReplaceAll(out, k, v)
 	}
diff --git a/server/config.yml b/server/config.yml
index 0f262ce..8abb5e8 100644
--- a/server/config.yml
+++ b/server/config.yml
@@ -44,7 +44,7 @@ imageGen:
   # 物种只写名字不写羽毛细节（细节会把镜头拉近，详见技术设计）。不带拍摄角度描述（2026-08-28）：
   # 角度描述会与目标位置耦合，仰拍使贴地目标沉底、固定俯拍构图单一——交由模型平视先验自由构图，
   # 目标自然落在中部/上部
-  promptTemplate: "野外大远景实拍：广角镜头下{scene}，{distanceWord}有且仅有{count}{species}的小小身影{sizeHint}在{action}，{species}离镜头很远，只是很小的{tone}轮廓，隐约可辨，画面绝大部分是{scene}，没有任何靠近镜头的动物，{light}，写实野外摄影，真实照片质感"
+  promptTemplate: "野外大远景实拍：广角镜头下{scene}，{distanceWord}有且仅有{count}{species}的小小身影{sizeHint}在{action}，{occlusion}，{species}离镜头很远，只是很小的{tone}轮廓，隐约可辨，画面绝大部分是{scene}，没有任何靠近镜头的动物，{light}，写实野外摄影，真实照片质感"
   speciesByDataset:            # 数据集名 → 物种池（表单未填动物名称时随机取一个）
     鸽子: [家鸽,火斑鸠,虎纹斑鸠,珠颈斑鸠,灰斑鸠]
     雉鸡: [雉鸡,白雉鸡]
@@ -91,6 +91,36 @@ imageGen:
     兔子: [低头啃食草叶, 警觉地竖起耳朵, 小步跳跃前行, 蹲坐不动, 啃食草叶]
     鸭子: [缓缓游动于水面, 低头潜入水中觅食, 水面梳理羽毛, 缓慢划水前行, 岸边泥滩觅食]
     鹌鹑: [低头啄食, 草丛间觅食, 小步疾走, 停歇不动]
+  # 遮挡描述池（模板 {occlusion} 占位，插在「在{action}」之后）：身体完全暴露的个体对识别/标注训练
+  # 无意义，每张必取一条（全池覆盖轻~重遮挡，程度随机）；遮挡物多样（杂草/树叶/枝条等），须与场景自洽
+  occlusions:                # 通用遮挡池（未配置 occlusionByDataset 的数据集回退此池）
+    - "身体部分被前景的杂草遮挡"
+    - "大半个身体隐入草丛，只露出头背"
+    - "身体被身前的杂草枝条半遮半掩"
+    - "只有头背部从茂密草丛中露出"
+    - "身体部分被低垂的树叶和枝条遮挡"
+    - "大半个身体隐在枝叶间，只露出头背"
+  occlusionByDataset:        # 数据集名 → 遮挡池（按动物习性与场景配置；未配置回退通用 occlusions）
+    雉鸡:
+      - "身体部分被前方的草丛和矮灌丛遮挡"
+      - "大半个身子隐入灌丛枝叶间，只露出头颈"
+      - "身体被身前的枯草丛半遮半掩"
+      - "只有头背部从密草丛中露出"
+      - "身体藏在树影下，被垂落的树叶半遮"
+    兔子:
+      - "身体部分被荒草遮挡，只露出头部"
+      - "大半身体隐入草丛，一对长耳竖在草尖上方"
+      - "身体被身前的枯黄野草半掩"
+      - "身体部分被林缘的树叶和枝条遮挡"
+    鸭子:
+      - "身体部分隐入岸边芦苇丛中"
+      - "半身没入水面，只露出头背"
+      - "身体被岸边的芦苇和杂草半遮"
+      - "身体部分隐入岸边垂落的枝叶间"
+    鹌鹑:
+      - "身体隐在密草丛中，只露出头部"
+      - "大半身体藏进草丛，隐约只见头和背部"
+      - "身体藏在草叶和落叶之间，只露出头部"
   # 目标尺寸提示（sizeHint）：按「体高 × focalPx ÷ 表单距离」算出目标在图中的像素高与占比，
   # 注入模板 {sizeHint}——把「厘米」换算成「米」再参与投影公式；focalPx = 图高/2 ÷ tan(vfov/2)
   assumedVfovDeg: 52          # 镜头垂直视场角(度)，固定为手机默认主摄（2026-08-28 用户定案；
diff --git a/server/data/observer.db b/server/data/observer.db
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diff --git a/server/技术设计.md b/server/技术设计.md
index 160139c..7e9e822 100644
--- a/server/技术设计.md
+++ b/server/技术设计.md
@@ -83,7 +83,7 @@ CREATE TABLE IF NOT EXISTS dataset_image (
   filename        TEXT NOT NULL,               -- 唯一文件名（防重名加时间戳后缀）
   source          TEXT NOT NULL DEFAULT 'manual',  -- manual | ai
   prompt          TEXT,                        -- AI 生成图记录提示词（追溯用）
-  labels_json     TEXT,                        -- 标注 JSON 数组（YOLO 归一化 xywh+类别+置信度），AI 自动标注与人工标注同存（人工可修改/清理），null/''/'[]'=未标注
+  labels_json     TEXT,                        -- 标注 JSON 数组（YOLO 归一化 xywh+类别+置信度），AI 自动标注直写、人工仅审核确认（2026-08-28：疑似确认/误检删除，不手动画框），null/''/'[]'=未标注
   created_at      TEXT NOT NULL,
   UNIQUE (dataset_id, filename)
 );
@@ -153,7 +153,7 @@ CREATE INDEX IF NOT EXISTS idx_model_training_status ON model_training(status);
 - v7 = `dataset` / `dataset_image` / `model_training` / `model_version` / `label_task` 新表（模型训练体系；各 `dao` init `CREATE TABLE IF NOT EXISTS` 自动建，此处记录 DDL 变更）
 - v8 = `dataset` 加 9 列（`cover`/`description`/`ai_endpoint`/`ai_model`/`train_host`/`train_user`/`train_password`/`train_key`）+ `label_task` 加 `filenames` 列（多选批量标注；`PRAGMA table_info` 逐列检测缺失才 `ALTER TABLE ... ADD COLUMN`，新库建表自带跳过）——**其中 6 列（ai/train 覆盖字段）为遗留列**：AI 标注/训练机 SSH 配置统一走 `config.yml`（`localAi` / `training.ssh`），新代码不读写，存量库保留不迁移
 - v9 = 标注存储从文件迁移入库：`dataset_image` 加 `labels_json`/`candidates_json` 两列（`common.EnsureColumn` 迁移），启动时把历史 `labels/<数据集>/*.txt` 解析入 `labels_json`（幂等：仅未迁移行处理），`boxes.json` 废弃；**历史 labels/ 目录与迁移代码已删除（2026-08-26：数据全部入表后无保留价值）**
-- v10 = 标注流程简化（撤销候选确认两阶段）：`ALTER TABLE dataset_image DROP COLUMN candidates_json`（`PRAGMA table_info` 检测列存在才 DROP，新库建表已无此列直接跳过；存量候选数据为空直接删）——AI 预标注结果直写 `labels_json`，人工可修改/清理全部标注框
+- v10 = 标注流程简化（撤销候选确认两阶段）：`ALTER TABLE dataset_image DROP COLUMN candidates_json`（`PRAGMA table_info` 检测列存在才 DROP，新库建表已无此列直接跳过；存量候选数据为空直接删）——AI 预标注结果直写 `labels_json`，人工仅审核确认（2026-08-28 定案：疑似框确认/误检删除，不手动画框）
 - v11 = 移除模型存档回退机制：`ALTER TABLE model_version DROP COLUMN model_file`（模型文件不落表：发布即写 `trainings/<数据集名>.tflite`，客户端固定下载）
 - v12 = 清理孤儿字段：`dataset` 删 6 列（`ai_endpoint`/`ai_model`/`train_host`/`train_user`/`train_password`/`train_key`，配置统一走 `config.yml` 的 `localAi`/`training.ssh`，零读写）+ `model_version` 删 `artifact_file`（zip 产物布局移除后无人写）+ `label_task` 删 `boxes_file`（标注已入库，候选框文件机制废弃）——均 `PRAGMA table_info` 检测列存在才 `ALTER TABLE ... DROP COLUMN`，新库建表已无此列自动跳过
 
@@ -307,7 +307,7 @@ Android 客户端启动 GET /api/v1/app/update（公开，无需 token；iOS 不
 
 ```
 管理端 数据集管理（上传 / AI 生成）→ 图片落服务器 workspace/datasets/<name>/
-      ├─► 二期：标注工作台（RF-DETR 预标注 + 人工确认）→ labels/
+      ├─► 二期：标注工作台（RF-DETR 预标注 + VLM 藏匿位补检，人工仅审核确认）
       └─► 发起训练 → 同步数据集到训练机 → runner 起训练 → 进度/日志/指标
             └─► 产物（best.tflite + best.pt 归档）拉回服务器
             └─► 发布 → model_version + workspace/model-latest.tflite（sha256）
@@ -316,7 +316,7 @@ Android 客户端启动 GET /api/v1/app/update（公开，无需 token；iOS 不
 
 ### 数据集存储与流转（决策：图片不进 DB、不提交 git）
 
-- 图片目录：`app.datasetDir`（默认 `./workspace/`）下 `datasets/<数据集名>/`（图片平铺，文件名唯一防重名）；标注存 `dataset_image.labels_json`（JSON 数组，每元素 `{class,cx,cy,w,h,confidence}` 归一化，AI 自动标注与人工标注同存、人工可修改/清理）；**历史 labels/ txt 目录与 yolo 暂存目录已删除（2026-08-26，标注全在 DB，训练集流式化不落本地盘）**
+- 图片目录：`app.datasetDir`（默认 `./workspace/`）下 `datasets/<数据集名>/`（图片平铺，文件名唯一防重名）；标注存 `dataset_image.labels_json`（JSON 数组，每元素 `{class,cx,cy,w,h,confidence}` 归一化，AI 自动标注直写、人工仅审核确认——2026-08-28：疑似确认/误检删除，不手动画框）；**历史 labels/ txt 目录与 yolo 暂存目录已删除（2026-08-26，标注全在 DB，训练集流式化不落本地盘）**
 - **封面命名规范（2026-08-26）**：封面文件不得固定命名 `cover`，上传时解码为 **jpg 格式 + UUIDv4 命名**（`common.UuidV4()` 生成，crypto/rand 无第三方依赖，落盘 `datasets/<name>/<uuid>.jpg`，旧封面删除）；DB `cover` 列只存文件名，前端用 DB 值拼 URL 零改动；删除/校验按 `isCoverName` 正则（UUID v4 + `.jpg` 后缀）识别，不认固定名——历史 `cover.jpg` 等存量封面启动时 `MigrateLegacyCovers` 幂等重命名迁移
 - DB 只存文件名/来源/prompt 等元数据（`dataset` / `dataset_image`），**禁止图片进库**
 - 生成/上传图片是**付费资产**：删除接口必须带前端确认文案（提示 AI 生成图有成本）；删除 = 删文件 + 删记录，目录清理
@@ -330,9 +330,9 @@ Android 客户端启动 GET /api/v1/app/update（公开，无需 token；iOS 不
   - `baseUrl`/`apiKey` 缺失时对应 provider 视为未配置，生成接口返回「图像生成服务未配置」
 - 尺寸参数由前端传（固定竖版 704x1248，比例与既有训练图 1152x2048 一致、32 倍数对齐）；**实测竖版图在 local-ai 上极慢（约 26 分钟/张，Qwen-Image 行列注意力对非方形输入效率差，与分辨率关系不大；方形 1024x1024 仅 5 分钟）**，超时与前端等待按此配置，一次建议 1 张
 - 生成同步执行（count 1..8，每张超时 `imageGen.timeoutSeconds`，默认 120s；localai 建议 600），逐张落盘 + 入库（记录 prompt 便于追溯）；中途失败返回错误（已成功的图保留，不删除——付费资产原则）
-- prompt 手填覆盖；留空走 `imageGen.promptTemplate` 通用模板逐张组装（物种池 speciesByDataset 按数据集名、数量词=animalCount、场景/动作/光线池随机，每张独立组装保持多样性；物种只写名字不写羽毛细节，细节会拉近镜头）；**遵守项目提示词规范：不得包含目标位置描述**（位置由模型自行推理，前端模板与校验文案落实此约束）
+- prompt 手填覆盖；留空走 `imageGen.promptTemplate` 通用模板逐张组装（物种池 speciesByDataset 按数据集名、数量词=animalCount、场景/动作/光线/遮挡池随机，每张独立组装保持多样性；物种只写名字不写羽毛细节，细节会拉近镜头）；**遵守项目提示词规范：不得包含目标位置描述**（位置由模型自行推理，前端模板与校验文案落实此约束）
 - **生成图距离/数量校验（2026-08-28）**：表单填 `distance`(米,固定值) 与 `animalCount` 后逐张启用；**距离校验已取消（2026-08-28）**：生成即入库，无逐张拒检；参数与公式见 git 历史（物理公式 d=体高×focalPx÷像素高）
-- **VLM 藏匿位补检（两阶段第二阶段）**：`/datasets/images/vlm-review` 单图接口；qwen3.8-9b(+mmproj) 以「图片+物种(生成 prompt 与物种池匹配)+场景光线线索+已确认框坐标(排除)」推理藏匿位，输出归一化 bbox JSON（范围校验+与已有框重叠去重），追加 ≤3 个 class=1 疑似框进同一 labels_json；VL 与 z-image 显存互斥（12G 装不下两者），批量补检在生成队列空闲后执行；qwen3.8-9b yaml 自带 mmproj 实为多模态模型（生成校验）；自动标注框数按置信度降序裁剪 ≤ animal_count（存 dataset_image.animal_count 列），估算距离 = 物种站高 × focalPx ÷ 最大框高像素（focalPx=`图高/2/tan(vfov/2)`，站高按物种查 `imageGen.speciesHeights`，单位 cm 换算为米，未配置物种代码兜底 35cm；**2026-08-28 语义修正**：speciesHeights 为站高（垂直尺度，鸟=脚到头顶、兽=蹲坐高），勿用体长——与提示词「高度」描述及检测框高自洽；避免跨物种共用一个标定系数）（K=1.8：6%≈30m、4.5%≈40m，K 隐含镜头视场角假设）；**镜头固定主摄（2026-08-28 定案）**：vfov 固定 `imageGen.assumedVfovDeg=52°`（手机默认主摄；曾随机 [15,90] 覆盖面广但目标像素波动 7~57px 不可控，曾试超广角 90 亦否决）；**场景/动作按数据集（2026-08-28）**：`imageGen.sceneByDataset`/`actionByDataset` 按数据集名配池（鸭→水面、雉鸡→灌丛草丛、兔→草坡荒地、鹌鹑→草丛），未配置回退通用 scenes/actions；**拍摄角度（2026-08-28，已废弃角度描述）**：不带任何拍摄角度描述——演进：固定「上部三分之一」区域描述对文生图模型遵循力弱（生成物落底）→ 加固定俯拍（目标上移成功但构图单一，用户否决）→ 池内随机俯/平/仰（仰拍使贴地目标必然沉底，用户否决）→ 只留俯拍/平视（用户仍嫌啰嗦）→ **完全去掉角度描述**（2026-08-28 定案）：交由模型平视先验自由构图，目标自然落在画面中部/上部；模板只靠「{species}离镜头很远，只是很小的{tone}轮廓」（物种名替代泛称「目标」，不限定鸟类——物种池含兔子等非鸟物种；`{tone}` 轮廓色词按 `imageGen.speciesTone.<物种>` 取，默认深色剪影，**白雉鸡配浅色**——白羽个体用「深色轮廓」描述会与模型渲染矛盾，2026-08-28）维持远景感，手填提示词仅追加数量约束；不合格丢弃重生成，单张上限 3 次（`consts.GenValidateMaxAttempts`），连续不合格整体报错（已合格张数保留）
+- **VLM 藏匿位补检（两阶段第二阶段）**：`/datasets/images/vlm-review` 单图接口；qwen3.8-9b(+mmproj) 以「图片+物种(生成 prompt 与物种池匹配)+场景光线线索+已确认框坐标(排除)」推理藏匿位，输出归一化 bbox JSON（范围校验+与已有框重叠去重），追加 ≤3 个 class=1 疑似框进同一 labels_json；VL 与 z-image 显存互斥（12G 装不下两者），批量补检在生成队列空闲后执行；qwen3.8-9b yaml 自带 mmproj 实为多模态模型（生成校验）；自动标注框数按置信度降序裁剪 ≤ animal_count（存 dataset_image.animal_count 列），估算距离 = 物种站高 × focalPx ÷ 最大框高像素（focalPx=`图高/2/tan(vfov/2)`，站高按物种查 `imageGen.speciesHeights`，单位 cm 换算为米，未配置物种代码兜底 35cm；**2026-08-28 语义修正**：speciesHeights 为站高（垂直尺度，鸟=脚到头顶、兽=蹲坐高），勿用体长——与提示词「高度」描述及检测框高自洽；避免跨物种共用一个标定系数）（K=1.8：6%≈30m、4.5%≈40m，K 隐含镜头视场角假设）；**镜头固定主摄（2026-08-28 定案）**：vfov 固定 `imageGen.assumedVfovDeg=52°`（手机默认主摄；曾随机 [15,90] 覆盖面广但目标像素波动 7~57px 不可控，曾试超广角 90 亦否决）；**场景/动作按数据集（2026-08-28）**：`imageGen.sceneByDataset`/`actionByDataset` 按数据集名配池（鸭→水面、雉鸡→灌丛草丛、兔→草坡荒地、鹌鹑→草丛），未配置回退通用 scenes/actions；**遮挡描述（2026-08-28）**：模板加 `{occlusion}` 占位（插在「在{action}」之后）——身体完全暴露的个体对识别/标注训练无意义，野外目标必然存在植被等遮挡；`imageGen.occlusionByDataset` 按数据集配遮挡池（雉鸡→草丛灌丛半掩、兔→荒草只露头耳、鸭→芦苇水面半没、鹌鹑→密草藏身），未配置回退通用 `imageGen.occlusions`；池内条目覆盖轻（身体半掩）~重（只露头背）遮挡，每张随机取一条，杜绝完全暴露；遮挡物多样（杂草/树叶/枝条，2026-08-28 补树叶）且须与 sceneByDataset 场景自洽；遮挡后可见部分小于站高，检测框高与距离估算按可见部分仍自洽（真实场景同此）；**拍摄角度（2026-08-28，已废弃角度描述）**：不带任何拍摄角度描述——演进：固定「上部三分之一」区域描述对文生图模型遵循力弱（生成物落底）→ 加固定俯拍（目标上移成功但构图单一，用户否决）→ 池内随机俯/平/仰（仰拍使贴地目标必然沉底，用户否决）→ 只留俯拍/平视（用户仍嫌啰嗦）→ **完全去掉角度描述**（2026-08-28 定案）：交由模型平视先验自由构图，目标自然落在画面中部/上部；模板只靠「{species}离镜头很远，只是很小的{tone}轮廓」（物种名替代泛称「目标」，不限定鸟类——物种池含兔子等非鸟物种；`{tone}` 轮廓色词按 `imageGen.speciesTone.<物种>` 取，默认深色剪影，**白雉鸡配浅色**——白羽个体用「深色轮廓」描述会与模型渲染矛盾，2026-08-28）维持远景感，手填提示词仅追加数量约束；不合格丢弃重生成，单张上限 3 次（`consts.GenValidateMaxAttempts`），连续不合格整体报错（已合格张数保留）
 
 ### 训练通道（runner 抽象，决策：同机/异机不确定 → 可配置）