fix(auth): prevent AttributeError when querying permissions for users with no role

deploy_patch.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+# === 配置项 ===
+SERVER="dxc@172.16.0.198"
+REMOTE_DIR="/opt/inventory-app"
+TIMESTAMP=$(date +%Y%m%d_%H%M)
+
+# 核心魔法：只定义你要发布的具体文件列表！
+FILES_TO_DEPLOY=(
+    "inventory-backend/app/api/v1/inbound/base.py"
+    "inventory-backend/app/services/inbound/base_service.py"
+    "inventory-web/src/api/material_base.ts"
+    "inventory-web/src/components/SpecHelper/index.vue"
+    "inventory-web/src/layout/index.vue"
+)
+
+echo "==================================================="
+echo "🚀 开始【局部补丁】部署 (仅覆盖特定的 ${#FILES_TO_DEPLOY[@]} 个文件)"
+echo "==================================================="
+
+# 1. 本地精准打包
+echo "[1/3] 正在提取指定文件并打包..."
+# tar 打包时会自动保留文件的原有目录结构
+tar -czf patch.tar.gz "${FILES_TO_DEPLOY[@]}"
+if [ $? -ne 0 ]; then echo "❌ 打包失败，请检查文件列表中的路径是否正确！"; exit 1; fi
+
+# 2. 传输到生产环境的 /tmp 目录
+echo "[2/3] 正在传输补丁包到服务器..."
+scp patch.tar.gz $SERVER:/tmp/patch.tar.gz
+
+# 3. 服务器执行覆盖与重启
+echo "[3/3] 正在服务器上覆盖指定文件并热更新 (可能需要输入密码)..."
+# 注意：这里直接在 $REMOTE_DIR 解压，tar 会按照原路径精准覆盖那 5 个文件，绝对不碰别的！
+ssh -t $SERVER "cd $REMOTE_DIR && \
+    sudo tar -xzf /tmp/patch.tar.gz && \
+    sudo docker compose -f docker-compose.prod.yml build backend frontend && \
+    sudo docker compose -f docker-compose.prod.yml up -d backend frontend && \
+    sudo rm /tmp/patch.tar.gz"
+
+# 清理本地临时压缩包
+rm patch.tar.gz
+
+echo "==================================================="
+echo "✅ 局部部署完成！请刷新服务器网页查看最新规格连号助手。"
+echo "==================================================="

docker-compose.yml

@@ -1,7 +1,6 @@
 version: '3.8'
 
 services:
-  # --- 数据库服务 ---
   db:
     image: postgres:15-alpine
     container_name: inventory_db
@@ -11,42 +10,35 @@ services:
       POSTGRES_PASSWORD: 1234
       POSTGRES_DB: inventory_system
     volumes:
-      # 数据持久化
       - ./pgdata_docker:/var/lib/postgresql/data
     ports:
-      - "5434:5432"
+      - "5435:5432"
 
-  # --- 后端 Flask 服务 ---
   backend:
     build:
-      context: ./inventory-backend  # 指向你的新后端目录
+      context: ./inventory-backend
     container_name: inventory_api
     restart: always
     ports:
       - "8000:8000"
     volumes:
-      - ./inventory-backend:/app # 挂载代码，实现热更新
-      # 【核心修改】显式挂载 uploads 目录，确保图片持久化且宿主机可见
+      - ./inventory-backend:/app
       - ./inventory-backend/uploads:/app/uploads
     command: gunicorn -c gunicorn.conf.py run:app --reload
     environment:
-      # Host 必须写 'db'
       DATABASE_URL: postgresql://test:1234@db:5432/inventory_system
     depends_on:
       - db
 
-  # --- 前端 Vue 开发服务 ---
   frontend:
     build:
       context: ./inventory-web
     container_name: inventory_ui
     restart: always
-    # 把本地代码挂载进去，实现“热更新”
     volumes:
       - ./inventory-web:/app
-      - /app/node_modules  # 排除 node_modules，防止冲突
-    # 开发模式端口通常是 5173
+      - /app/node_modules
     ports:
-      - "5173:5173"
+      - "5175:5173"
     depends_on:
-      - backend
+      - backend

inventory-backend/app/services/auth_service.py

@@ -345,9 +345,13 @@ class AuthService:
             'elements': ['inbound_buy:unit_price', ...]
         }
         """
+        # 防御性编程：role_code 为空时直接返回空权限，避免后续 SQL 崩溃
+        if not role_code:
+            return {'menus': [], 'elements': []}
+
         # 超级管理员返回所有权限（通配符）
         from app.utils.constants import UserRole
-        if role_code and role_code.upper() == UserRole.SUPER_ADMIN:
+        if role_code.upper() == UserRole.SUPER_ADMIN:
             # 返回通配符，表示拥有所有菜单和元素权限
             return {
                 'menus': ['*'],

inventory-web/package.json

@@ -4,7 +4,7 @@
   "version": "0.0.0",
   "type": "module",
   "scripts": {
-    "dev": "vite",
+    "dev": "vite --host",
     "build": "vite build",
     "preview": "vite preview"
   },
@@ -35,4 +35,4 @@
   "overrides": {
     "vite": "npm:rolldown-vite@7.2.5"
   }
-}
+}

sync_db.sh

@@ -1,21 +1,22 @@
 #!/bin/bash
 
 # ==========================================
-# 1. 本地 WSL 数据库配置 (根据你之前的数据)
+# 1. 本地 WSL 数据库配置
 # ==========================================
 LOCAL_CONTAINER="inventory_db"
 LOCAL_DB_USER="test"
 LOCAL_DB_NAME="inventory_system"
 
 # ==========================================
-# 2. 远程服务器 SSH 配置 (根据你的截图)
+# 2. 远程服务器 SSH 配置
 # ==========================================
 REMOTE_USER="dxc"
 REMOTE_HOST="172.16.0.198"
 REMOTE_PORT="22"
+REMOTE_DIR="/opt/inventory-app" # 用于存放备份
 
 # ==========================================
-# 3. 远程服务器 Docker 配置 (根据你的 docker-compose.prod.yml)
+# 3. 远程服务器 Docker 配置
 # ==========================================
 REMOTE_CONTAINER="inventory_db_prod"
 REMOTE_DB_USER="prod_user"
@@ -26,14 +27,28 @@ REMOTE_DB_NAME="inventory_system"
 TIMESTAMP=$(date +%Y%m%d_%H%M%S)
 DUMP_FILE="db_sync_${TIMESTAMP}.sql.gz"
 LOCAL_DUMP_PATH="/tmp/${DUMP_FILE}"
+REMOTE_BACKUP_FILE="${REMOTE_DIR}/data_copy/DB_BACKUP_${TIMESTAMP}.sql.gz"
 
 echo "========================================================"
 echo " 🔄 开始同步 WSL 数据库到远程服务器 (${REMOTE_HOST})"
+echo " ⚠️ 注意：线上旧数据将被完全覆盖！"
 echo "========================================================"
 
+# --- 新增：步骤 0: 远程服务器数据备份 ---
+echo -e "\n[0/4] 🛡️ 正在备份线上服务器数据库..."
+ssh -p ${REMOTE_PORT} ${REMOTE_USER}@${REMOTE_HOST} << EOF
+    mkdir -p ${REMOTE_DIR}/data_copy
+    # 导出线上数据作为备份
+    docker exec -e PGPASSWORD="${REMOTE_DB_PASS}" ${REMOTE_CONTAINER} pg_dump -U ${REMOTE_DB_USER} -d ${REMOTE_DB_NAME} -O -x | gzip > ${REMOTE_BACKUP_FILE}
+    echo "  -> 线上备份已保存至: ${REMOTE_BACKUP_FILE}"
+EOF
+if [ $? -ne 0 ]; then
+    echo "❌ 线上备份失败！为保证数据安全，同步已终止！"
+    exit 1
+fi
+
 # --- 步骤 1: 本地导出 ---
 echo -e "\n[1/4] 📦 正在本地打包数据库..."
-# 注意：这里使用 pg_dump 导出，为了兼容性，排除可能引起冲突的权限所有者信息 (-O -x)
 docker exec ${LOCAL_CONTAINER} pg_dump -U ${LOCAL_DB_USER} -d ${LOCAL_DB_NAME} -O -x | gzip > ${LOCAL_DUMP_PATH}
 if [ $? -ne 0 ]; then
     echo "❌ 本地数据库导出失败！请检查本地 inventory_db 容器是否正常运行。"
@@ -57,7 +72,6 @@ ssh -p ${REMOTE_PORT} ${REMOTE_USER}@${REMOTE_HOST} << EOF
     docker cp /tmp/${DUMP_FILE} ${REMOTE_CONTAINER}:/tmp/${DUMP_FILE}
 
     echo "  -> 危险操作：清空服务器旧数据环境..."
-    # 传入 PGPASSWORD 环境变量以防密码拦截
     docker exec -e PGPASSWORD="${REMOTE_DB_PASS}" ${REMOTE_CONTAINER} psql -U ${REMOTE_DB_USER} -d ${REMOTE_DB_NAME} -c "DROP SCHEMA public CASCADE; CREATE SCHEMA public; GRANT ALL ON SCHEMA public TO ${REMOTE_DB_USER};"
 
     echo "  -> 正在导入最新数据..."
@@ -74,4 +88,4 @@ rm ${LOCAL_DUMP_PATH}
 
 echo -e "\n========================================================"
 echo "🎉 数据库全量替换成功！快去刷新你的线上系统看看吧！"
-echo "========================================================"
+echo "========================================================"

库研操作/导入数据库.py

@@ -0,0 +1,102 @@
+import pandas as pd
+import psycopg2
+
+# 1. 数据库配置
+DB_CONFIG = {
+    'dbname': 'inventory_system',
+    'user': 'test',
+    'password': '1234',
+    'host': 'localhost',
+    'port': '5435'
+}
+
+# 2. Excel 文件路径
+EXCEL_FILE = '筛选后的库存统计.xlsx'
+
+
+def fix_category_data_no_nan():
+    try:
+        print("正在读取 Excel 文件...")
+
+        # 【修改点 1】：明确限制只读取到第四级
+        possible_category_cols = ['类别一级', '类别二级', '类别三级', '类别四级']
+
+        df_header = pd.read_excel(EXCEL_FILE, nrows=0)
+        actual_category_cols = [col for col in possible_category_cols if col in df_header.columns]
+
+        needed_columns = ['资产名称', '规格型号'] + actual_category_cols
+
+        df = pd.read_excel(EXCEL_FILE, dtype=str, usecols=lambda x: x in needed_columns)
+        df = df.where(pd.notnull(df), None)
+        df = df.drop_duplicates(subset=['资产名称', '规格型号'])
+
+        print(f"发现了 {len(df)} 种独立物料，准备修复类别并清除 'nan'...")
+
+        conn = psycopg2.connect(**DB_CONFIG)
+        cur = conn.cursor()
+
+        update_count = 0
+
+        for index, row in df.iterrows():
+            name = row.get('资产名称')
+            spec_model = row.get('规格型号')
+
+            # 清理规格型号，防止它也被 pandas 变成了 'nan'
+            clean_spec = None if pd.isna(spec_model) or str(spec_model).lower() == 'nan' else str(spec_model).strip()
+
+            if not name or str(name).lower() == 'nan':
+                continue
+
+            # --- 核心逻辑：只拼接前4级，并且严格过滤 nan ---
+            category_parts = []
+            for col in actual_category_cols:
+                val = row.get(col)
+                if val is not None:
+                    str_val = str(val).strip()
+                    # 【修改点 2】：增加对 'nan' 和 'None' 字符串的拦截
+                    if str_val != '' and str_val.lower() != 'nan' and str_val.lower() != 'none':
+                        category_parts.append(str_val)
+
+            full_category = "/".join(category_parts)
+
+            if not full_category:
+                continue
+
+            prefixed_name = f"库研*{name}"
+            prefixed_spec = f"KY*{clean_spec}" if clean_spec else None
+
+            # 执行更新操作
+            update_query = """
+                           UPDATE material_base
+                           SET category = %s
+                           WHERE (name = %s OR name = %s)
+                             AND (
+                               (spec_model = %s OR spec_model = %s)
+                                   OR (spec_model IS NULL AND %s IS NULL)
+                               ) \
+                           """
+
+            cur.execute(update_query, (
+                full_category,
+                name, prefixed_name,
+                clean_spec, prefixed_spec, clean_spec
+            ))
+
+            update_count += cur.rowcount
+
+        conn.commit()
+        print(f"✅ 完美修复！清除了讨厌的 'nan'，共修正了 {update_count} 条记录。")
+
+    except Exception as e:
+        print(f"❌ 发生错误: {e}")
+        if 'conn' in locals() and conn:
+            conn.rollback()
+    finally:
+        if 'cur' in locals() and cur:
+            cur.close()
+        if 'conn' in locals() and conn:
+            conn.close()
+
+
+if __name__ == "__main__":
+    fix_category_data_no_nan()

库研操作/筛选.py

@@ -0,0 +1,29 @@
+import pandas as pd
+
+# 1. 读取您的Excel文件
+file_path = '库存统计_20260413_094414.xlsx'
+df = pd.read_excel(file_path)
+
+# 指定要进行筛选的列名（根据您的截图，列名应为“仓库位置”）
+col_name = '仓库位置'
+
+# 2. 数据清洗：确保该列都是字符串格式，并处理可能存在的空值(NaN)
+# 这一步是为了防止后续字符串操作报错
+df[col_name] = df[col_name].astype(str)
+
+# 3. 进行筛选
+# 条件 A: str.count('/') == 2 (说明通过斜杠分割后只有3个部分，即3层)
+# 条件 B: str.endswith('/1') (说明最后是以 /1 结尾的，即最后一层是1)
+condition = (df[col_name].str.count('/') == 2) & (df[col_name].str.endswith('/1'))
+
+# 将满足条件的数据提取出来
+filtered_df = df[condition]
+
+# 4. 打印查看筛选后的前几行结果
+print("筛选出的符合要求的数据如下：")
+print(filtered_df[[col_name]])
+
+# 5. （可选）将筛选后的结果保存为新的 Excel 文件
+output_path = '筛选后的库存统计.xlsx'
+filtered_df.to_excel(output_path, index=False)
+print(f"\n筛选完成，结果已保存至：{output_path}")