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

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}")