diff --git a/deploy_patch.sh b/deploy_patch.sh new file mode 100644 index 0000000..d386336 --- /dev/null +++ b/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 "===================================================" \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 80f4542..764ed1e 100644 --- a/docker-compose.yml +++ b/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 \ No newline at end of file + - backend diff --git a/inventory-backend/app/services/auth_service.py b/inventory-backend/app/services/auth_service.py index 07c865b..cc426af 100644 --- a/inventory-backend/app/services/auth_service.py +++ b/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': ['*'], diff --git a/inventory-web/package.json b/inventory-web/package.json index e702802..d9af0e6 100644 --- a/inventory-web/package.json +++ b/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" } -} +} \ No newline at end of file diff --git a/sync_db.sh b/sync_db.sh index 8334baa..489c628 100644 --- a/sync_db.sh +++ b/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 "========================================================" \ No newline at end of file diff --git a/库研操作/导入数据库.py b/库研操作/导入数据库.py new file mode 100644 index 0000000..0819646 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/库研操作/库存统计_20260413_094414.xlsx b/库研操作/库存统计_20260413_094414.xlsx new file mode 100644 index 0000000..c2f95ef Binary files /dev/null and b/库研操作/库存统计_20260413_094414.xlsx differ diff --git a/库研操作/筛选.py b/库研操作/筛选.py new file mode 100644 index 0000000..c6884f0 --- /dev/null +++ b/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}") \ No newline at end of file diff --git a/库研操作/筛选后的库存统计.xlsx b/库研操作/筛选后的库存统计.xlsx new file mode 100644 index 0000000..9b86047 Binary files /dev/null and b/库研操作/筛选后的库存统计.xlsx differ