refactor: step 3 - decompose ETL pipeline into modular services/scud_etl package
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"""
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===============================================================================
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FILE: services/scud_etl/anomaly_detector.py
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PROJECT: SCUD Orion AI (Unified Architecture)
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MODULE: services / scud_etl
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ROLE: Автоматическое выявление аномалий и конфликтов между 1С:ЗУП и СКУД.
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AI-CONTEXT-ANCHORS:
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- ANCHOR[ANOMALY_DETECTOR_CORE]: Проверка физического присутствия в отпуске и перемещений без входа.
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===============================================================================
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"""
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import pandas as pd
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from typing import List, Dict, Any, Optional
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ALLOWED_WORK_TRIP_KEYWORDS = ['командировк', 'разъездн', 'поездк']
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# ANCHOR[ANOMALY_DETECTOR_CORE]
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def detect_all_anomalies(
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merged_df: pd.DataFrame,
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static_reasons_dict: dict,
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kb_rules: List[str],
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scud_fios_set: Optional[set] = None
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) -> List[Dict[str, Any]]:
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"""Выявляет конфликты и аномалии между источниками СКУД и 1С."""
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anomalies = []
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kb_rules_text = " ".join(kb_rules).lower() if kb_rules else ""
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for idx, row in merged_df.iterrows():
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fio = row.get('Сотрудник', row.get('fio_clean', ''))
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fio_clean = row.get('fio_clean', '')
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is_present = row.get('Пришел', False)
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is_exc = row.get('is_excluded', False)
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reason_1c = str(row.get('Вид_отсутствия', '')).strip()
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has_1c_reason = pd.notna(row.get('Вид_отсутствия')) and reason_1c != '' and not reason_1c.startswith('Исключение')
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anom_flag = row.get('anomaly_flag', 'NONE')
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is_fio_whitelisted = fio_clean.lower() in kb_rules_text
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# 1. Присутствие при официальном отсутствии
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if is_present and has_1c_reason:
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is_allowed_trip = any(kw in reason_1c.lower() for kw in ALLOWED_WORK_TRIP_KEYWORDS)
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if not is_allowed_trip and not is_fio_whitelisted:
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anomalies.append({
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"type": "ФИЗИЧЕСКОЕ ПРИСУТСТВИЕ ПРИ ОФИЦИАЛЬНОМ ОТСУТСТВИИ",
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"fio": fio,
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"details": f"Сотрудник пришел по СКУД, но в 1С оформлен документ: '{reason_1c}'"
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})
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if is_exc and not has_1c_reason:
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continue
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# 2. Перемещение внутри здания без отметки входа на КПП
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if anom_flag == 'ANOMALY_NO_IN_HAS_ACTIVITY':
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first_act = row.get('Первая_активность', '—')
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anomalies.append({
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"type": "АНОМАЛИЯ СКУД: ПЕРЕМЕЩЕНИЕ БЕЗ ВХОДА",
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"fio": fio,
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"details": f"Отсутствует регистрация входа на КПП при зафиксированной первой активности в {first_act}"
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})
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# 3. Сотрудник в штате 1С, но карты/профиля в СКУД нет
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if scud_fios_set is not None:
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if fio_clean not in scud_fios_set and not has_1c_reason:
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anomalies.append({
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"type": "АНОМАЛИЯ УЧЕТА: СОТРУДНИК ОТСУТСТВУЕТ В СКУД ОРИОН PRO",
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"fio": fio,
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"details": f"Сотрудник числится в Штатном расписании 1С ({row.get('Подразделение', '—')}), но отсутствует в СКУД"
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})
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return anomalies
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"""
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===============================================================================
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FILE: services/scud_etl/merger.py
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PROJECT: SCUD Orion AI (Unified Architecture)
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MODULE: services / scud_etl
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ROLE: Агрегация проходов, сопоставление исключений и мердж таблиц 1С:ЗУП и СКУД.
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AI-CONTEXT-ANCHORS:
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- ANCHOR[MERGER_EXCEPTIONS]: Наложение флага исключений из exceptions.json.
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- ANCHOR[MERGER_AGGREGATION]: Агрегация множественных проходов до уникального ФИО.
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- ANCHOR[MERGER_BUILD_DATASET]: Сборка итогового датасета для сводок и отчетов.
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===============================================================================
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"""
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import os
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import json
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import pandas as pd
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from config import normalize_fio, DATA_DIR
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# ANCHOR[MERGER_EXCEPTIONS]
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def load_exceptions_config() -> dict:
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"""Загружает exceptions.json из корня проекта."""
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root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
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json_path = os.path.join(root_dir, "exceptions.json")
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if not os.path.exists(json_path):
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return {}
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try:
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with open(json_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except Exception:
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return {}
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def apply_exceptions_from_json(df: pd.DataFrame, exceptions_cfg: dict) -> pd.DataFrame:
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"""Быстрая разметка флага is_excluded на основе exceptions.json."""
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if df is None or df.empty or not exceptions_cfg:
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if df is not None:
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df['is_excluded'] = False
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return df
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deps = [d.strip().lower() for d in exceptions_cfg.get("departments", []) if d]
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exact_pos = [p.strip().lower() for p in exceptions_cfg.get("positions", []) if p]
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pos_kw = [k.strip().lower() for k in exceptions_cfg.get("position_keywords", []) if k]
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exc_fios = [normalize_fio(f) for f in exceptions_cfg.get("fio", []) if f]
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df['is_excluded'] = False
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for idx, row in df.iterrows():
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fio_clean = row.get('fio_clean', '')
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dep_1c = str(row.get('Подразделение', '')).lower()
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dep_scud = str(row.get('department_scud', row.get('department', ''))).lower()
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pos = str(row.get('Должность', '')).lower()
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is_fio_exc = fio_clean in exc_fios
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is_pos_exc = (pos in exact_pos) or any(k in pos for k in pos_kw if k) if pos else False
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is_dep_exc = any(d in dep_1c or d in dep_scud for d in deps) if deps else False
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if is_fio_exc or is_dep_exc or is_pos_exc:
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df.at[idx, 'is_excluded'] = True
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return df
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# ANCHOR[MERGER_AGGREGATION]
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def aggregate_scud_by_employee(df: pd.DataFrame) -> pd.DataFrame:
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"""Агрегирует проходы СКУД по уникальным сотрудникам."""
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if df is None or df.empty or 'fio_clean' not in df.columns:
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return df
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aggregated = []
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for fio_clean, group in df.groupby('fio_clean', sort=False):
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is_present = group['Пришел'].any() if 'Пришел' in group.columns else False
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if is_present and 'Пришел' in group.columns:
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present_rows = group[group['Пришел'] == True]
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best_row = present_rows.iloc[0].to_dict() if not present_rows.empty else group.iloc[0].to_dict()
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else:
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best_row = group.iloc[0].to_dict()
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best_row['Пришел'] = is_present
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aggregated.append(best_row)
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return pd.DataFrame(aggregated)
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def filter_report_dataframe(merged_df: pd.DataFrame) -> pd.DataFrame:
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"""Исключает подрядчиков и сотрудников без пропуска из детального отчета."""
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if merged_df is None or merged_df.empty:
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return merged_df
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has_1c_reason = (
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merged_df['Вид_отсутствия'].notna() &
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(merged_df['Вид_отсутствия'].astype(str).str.strip() != '') &
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(~merged_df['Вид_отсутствия'].astype(str).str.startswith('Исключение'))
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)
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is_not_excluded = merged_df.get('is_excluded', False) == False
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is_not_no_pass = merged_df.get('no_scud_pass', False) == False
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return merged_df[(is_not_excluded & is_not_no_pass) | has_1c_reason].copy()
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def load_static_reason_workers() -> dict:
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"""Загружает реестр удаленщиков из CSV."""
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static_path = os.path.join(DATA_DIR, "static_reason_workers.csv")
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if not os.path.exists(static_path):
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return {}
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try:
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df_static = pd.read_csv(static_path, encoding='utf-8')
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if 'fio' in df_static.columns and 'reason' in df_static.columns:
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df_static['fio_clean'] = df_static['fio'].apply(normalize_fio)
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return dict(zip(df_static['fio_clean'], df_static['reason']))
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except Exception:
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pass
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return {}
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@@ -0,0 +1,139 @@
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"""
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===============================================================================
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FILE: services/scud_etl/pipeline.py
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PROJECT: SCUD Orion AI (Unified Architecture)
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MODULE: services / scud_etl
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ROLE: Оркестратор этапов контроллинга (Загрузка -> Сверка -> Отчеты -> SQLite).
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AI-CONTEXT-ANCHORS:
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- ANCHOR[PIPELINE_RUN_CONTROLLING]: Главная функция выполнения ETL-конвейера.
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===============================================================================
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"""
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import os
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from datetime import datetime, timedelta
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import pandas as pd
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from config import DATE_TODAY, DATE_YESTERDAY, OUTPUT_DIR
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from core.database import (
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save_scud_to_db, save_staff_to_db, save_absences_to_db,
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save_anomalies_to_db, load_scud_from_db_by_snapshot, get_latest_snapshot_time
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)
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from services.data_loader import load_1c_data_smart
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from services.excel_exporter import generate_summary_excel, generate_detailed_excel
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from services.ai_verifier import ai_verify_scud_against_staff, analyze_scud_mass_failure_ai
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from services.text_reporter import generate_markdown_report
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from services.feedback_loop import review_ai_decisions
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from services.knowledge_base import load_knowledge_base
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from .merger import (
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load_exceptions_config, apply_exceptions_from_json,
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aggregate_scud_by_employee, filter_report_dataframe, load_static_reason_workers
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)
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from .anomaly_detector import detect_all_anomalies
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# ANCHOR[PIPELINE_RUN_CONTROLLING]
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def run_controlling_pipeline(snapshot_param: str = None, skip_export: bool = False, debug: bool = False, has_today_1c: bool = True) -> None:
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"""Выполняет полный цикл сверки СКУД ⟷ 1С и сохранение результатов."""
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kb_rules = load_knowledge_base().get("rules", [])
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exceptions_cfg = load_exceptions_config()
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static_reasons = load_static_reason_workers()
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print("[2/5] Загрузка данных из СКУД, 1С:ЗУП, реестра причин и исключений...")
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# 1. Определение дат целевого снапшота и предыдущей смены
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if snapshot_param or skip_export:
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snap_to_use = snapshot_param or get_latest_snapshot_time()
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raw_scud_today_df = load_scud_from_db_by_snapshot(None, snapshot_param=snap_to_use)
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if not raw_scud_today_df.empty and 'log_date' in raw_scud_today_df.columns:
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target_date_str = str(raw_scud_today_df['log_date'].iloc[0])
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else:
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target_date_str = DATE_TODAY
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else:
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target_date_str = DATE_TODAY
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raw_scud_today_df = load_scud_from_db_by_snapshot(target_date_str, snapshot_param=snapshot_param)
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dt_target = datetime.strptime(target_date_str, "%d.%m.%Y")
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dt_yesterday = dt_target - timedelta(days=3 if dt_target.weekday() == 0 else 1)
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yesterday_date_str = dt_yesterday.strftime("%d.%m.%Y")
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print(f"[📸] СНАПШОТ ОПРЕДЕЛЕН: Целевая дата = {target_date_str}, Накануне = {yesterday_date_str}\n")
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raw_scud_yesterday_df = load_scud_from_db_by_snapshot(yesterday_date_str, snapshot_param=None)
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df_staff_yesterday, df_absent_yesterday = load_1c_data_smart(yesterday_date_str, use_db=True)
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df_staff_today, df_absent_today = load_1c_data_smart(target_date_str, use_db=True)
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# 2. Этап 3: Обработка ВЧЕРА (Детальный отчет)
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print(f"[3/5] Обработка и построение детального отчета за ВЧЕРА ({yesterday_date_str})...")
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if df_staff_yesterday is not None:
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save_staff_to_db(df_staff_yesterday, yesterday_date_str)
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if df_absent_yesterday is not None:
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save_absences_to_db(df_absent_yesterday, yesterday_date_str)
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# Проверка опечаток ФИО через AI-аудитор
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staff_fios_y_clean = df_staff_yesterday['fio_clean'].dropna().tolist() if df_staff_yesterday is not None else []
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if not raw_scud_yesterday_df.empty:
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raw_scud_yesterday_df['Пришел'] = raw_scud_yesterday_df['is_present'].astype(int) == 1 if 'is_present' in raw_scud_yesterday_df.columns else False
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unrecog = raw_scud_yesterday_df[~raw_scud_yesterday_df['fio_clean'].isin(staff_fios_y_clean)]['fio_clean'].tolist()
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fio_map = ai_verify_scud_against_staff(unrecog, staff_fios_y_clean)
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if fio_map:
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raw_scud_yesterday_df['fio_clean'] = raw_scud_yesterday_df['fio_clean'].apply(lambda x: fio_map[x]['staff_fio'] if x in fio_map else x)
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raw_scud_yesterday_df = aggregate_scud_by_employee(raw_scud_yesterday_df)
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merged_y = (df_staff_yesterday.copy() if df_staff_yesterday is not None else pd.DataFrame())
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if not merged_y.empty:
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if not raw_scud_yesterday_df.empty:
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merged_y = merged_y.merge(
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raw_scud_yesterday_df[['fio_clean', 'Пришел', 'Начало_дня', 'Первая_активность', 'Конец_дня', 'Находился_в_здании', 'anomaly_flag']],
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on='fio_clean', how='left'
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)
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if df_absent_yesterday is not None and not df_absent_yesterday.empty:
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merged_y = merged_y.merge(df_absent_yesterday[['fio_clean', 'Вид_отсутствия']], on='fio_clean', how='left')
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merged_y['Пришел'] = merged_y['Пришел'].fillna(False) if 'Пришел' in merged_y.columns else False
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if 'Сотрудник' not in merged_y.columns:
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merged_y['Сотрудник'] = merged_y.get('ФИО', merged_y['fio_clean'])
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merged_y = apply_exceptions_from_json(merged_y, exceptions_cfg)
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scud_fios_y = set(raw_scud_yesterday_df['fio_clean'].dropna().tolist()) if not raw_scud_yesterday_df.empty else set()
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anomalies_y = detect_all_anomalies(merged_y, static_reasons, kb_rules, scud_fios_set=scud_fios_y)
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save_anomalies_to_db(anomalies_y, yesterday_date_str)
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filtered_y = filter_report_dataframe(merged_y)
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generate_detailed_excel(merged_df=filtered_y, date_str=yesterday_date_str)
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save_scud_to_db(merged_y, yesterday_date_str, snapshot_time=f"{dt_yesterday.strftime('%Y-%m-%d')} 22:00:00", is_yesterday=True)
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print(f"[✓] Детальный отчет за вчера сформирован и зафиксирован в SQLite за {yesterday_date_str}")
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# 3. Этап 4 & 5: Обработка СЕГОДНЯ (Ежедневная сводка)
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if has_today_1c and df_staff_today is not None and df_absent_today is not None:
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print(f"\n[4/5] Обработка и построение Ежедневной сводки за {target_date_str}...")
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save_staff_to_db(df_staff_today, target_date_str)
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save_absences_to_db(df_absent_today, target_date_str)
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raw_scud_today_df = aggregate_scud_by_employee(raw_scud_today_df)
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merged_t = df_staff_today.copy()
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if not raw_scud_today_df.empty:
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merged_t = merged_t.merge(
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raw_scud_today_df[['fio_clean', 'Пришел', 'Начало_дня', 'Первая_активность', 'Конец_дня', 'Находился_в_здании', 'anomaly_flag']],
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on='fio_clean', how='left'
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)
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merged_t = merged_t.merge(df_absent_today[['fio_clean', 'Вид_отсутствия']], on='fio_clean', how='left')
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merged_t['Пришел'] = merged_t['Пришел'].fillna(False) if 'Пришел' in merged_t.columns else False
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if 'Сотрудник' not in merged_t.columns:
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merged_t['Сотрудник'] = merged_t.get('ФИО', merged_t['fio_clean'])
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merged_t = apply_exceptions_from_json(merged_t, exceptions_cfg)
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scud_fios_t = set(raw_scud_today_df['fio_clean'].dropna().tolist()) if not raw_scud_today_df.empty else set()
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anomalies_t = detect_all_anomalies(merged_t, static_reasons, kb_rules, scud_fios_set=scud_fios_t)
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save_anomalies_to_db(anomalies_t, target_date_str)
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print("[5/5] Сохранение Ежедневной сводки...")
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generate_summary_excel(merged_df=merged_t, date_str=target_date_str)
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print(f"[✓] Ежедневная сводка сохранена за {target_date_str}")
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else:
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print(f"\n[ℹ️] Формирование Ежедневной сводки за {target_date_str} ПРОПУЩЕНО.")
|
||||
@@ -0,0 +1,116 @@
|
||||
"""
|
||||
===============================================================================
|
||||
FILE: services/scud_etl/sql_queries.py
|
||||
PROJECT: SCUD Orion AI (Unified Architecture)
|
||||
MODULE: services / scud_etl
|
||||
ROLE: Хранилище сырых SQL-шаблонов для выгрузки из MS SQL Server (СКУД Орион Pro).
|
||||
|
||||
AI-CONTEXT-ANCHORS:
|
||||
- ANCHOR[SQL_SCUD_EXPORT_TEMPLATE]: T-SQL запрос с расчетом первой активности и длительности.
|
||||
===============================================================================
|
||||
"""
|
||||
|
||||
# ANCHOR[SQL_SCUD_EXPORT_TEMPLATE]
|
||||
SCUD_EXPORT_QUERY_TEMPLATE = r"""
|
||||
DECLARE @InputDate DATE = '{target_date}';
|
||||
DECLARE @TargetDate DATE = @InputDate;
|
||||
|
||||
DECLARE @StartDate DATETIME = CAST(@TargetDate AS DATETIME);
|
||||
DECLARE @EndDate DATETIME = DATEADD(SECOND, -1, DATEADD(DAY, 1, @StartDate));
|
||||
|
||||
WITH DailyLogs AS (
|
||||
SELECT
|
||||
log.HozOrgan AS EmployeeID,
|
||||
log.TimeVal,
|
||||
log.Event,
|
||||
log.Mode,
|
||||
CASE
|
||||
WHEN log.Mode = 2 OR log.Event IN (29, 27, 33) THEN 'OUT'
|
||||
WHEN log.Mode = 1 OR log.Event IN (28, 26, 32) THEN 'IN'
|
||||
ELSE 'OTHER'
|
||||
END AS Direction,
|
||||
ROW_NUMBER() OVER (PARTITION BY log.HozOrgan ORDER BY log.TimeVal DESC) AS RowNumDesc
|
||||
FROM pLogData log WITH (NOLOCK)
|
||||
WHERE log.TimeVal BETWEEN @StartDate AND @EndDate
|
||||
AND log.HozOrgan IS NOT NULL
|
||||
AND log.HozOrgan > 0
|
||||
AND log.Event IN (26, 27, 28, 29, 32, 33, 54, 55, 64, 65)
|
||||
),
|
||||
Passages AS (
|
||||
SELECT
|
||||
EmployeeID,
|
||||
MIN(TimeVal) AS FirstRawEvent,
|
||||
MAX(TimeVal) AS LastRawEvent,
|
||||
MIN(CASE WHEN Direction = 'IN' THEN TimeVal END) AS FirstIn,
|
||||
MAX(CASE WHEN Direction = 'OUT' THEN TimeVal END) AS LastOut,
|
||||
MAX(CASE WHEN RowNumDesc = 1 THEN Direction END) AS LastEventType
|
||||
FROM DailyLogs
|
||||
GROUP BY EmployeeID
|
||||
)
|
||||
SELECT
|
||||
N'ЛЕНМОРНИИПРОЕКТ' AS [Фирма],
|
||||
ISNULL(CAST(div.Name AS NVARCHAR(255)), N'Без подразделения') AS [Подразделение],
|
||||
LTRIM(RTRIM(
|
||||
ISNULL(CAST(p.Name AS NVARCHAR(255)), N'') +
|
||||
CASE WHEN p.FirstName IS NOT NULL AND CAST(p.FirstName AS NVARCHAR(255)) <> ''
|
||||
THEN N' ' + CAST(p.FirstName AS NVARCHAR(255)) ELSE N'' END +
|
||||
CASE WHEN p.MidName IS NOT NULL AND CAST(p.MidName AS NVARCHAR(255)) <> ''
|
||||
THEN N' ' + CAST(p.MidName AS NVARCHAR(255)) ELSE N'' END
|
||||
)) AS [Сотрудник],
|
||||
ISNULL(CAST(post.Name AS NVARCHAR(255)), N'—') AS [Должность],
|
||||
ISNULL(CAST(p.TabNumber AS NVARCHAR(50)), N'—') AS [Таб_№],
|
||||
CONVERT(VARCHAR(10), @TargetDate, 104) AS [Дата],
|
||||
ISNULL(CAST(CONVERT(VARCHAR(8), pass.FirstIn, 108) AS NVARCHAR(20)), N'Нет входа') AS [Начало_дня],
|
||||
CASE
|
||||
WHEN pass.FirstIn IS NULL AND pass.FirstRawEvent IS NOT NULL
|
||||
THEN CAST(CONVERT(VARCHAR(8), pass.FirstRawEvent, 108) AS NVARCHAR(20))
|
||||
ELSE N'—'
|
||||
END AS [Первая_активность],
|
||||
CASE
|
||||
WHEN @TargetDate = CAST(GETDATE() AS DATE) AND (pass.LastEventType = 'IN' OR pass.LastOut IS NULL OR pass.LastOut <= pass.FirstIn)
|
||||
THEN N'Нет выхода'
|
||||
WHEN pass.LastOut IS NOT NULL AND pass.LastOut > pass.FirstIn
|
||||
THEN CAST(CONVERT(VARCHAR(8), pass.LastOut, 108) AS NVARCHAR(20))
|
||||
WHEN @TargetDate < CAST(GETDATE() AS DATE) AND pass.LastRawEvent IS NOT NULL AND pass.LastRawEvent > ISNULL(pass.FirstIn, pass.FirstRawEvent)
|
||||
THEN CAST(CONVERT(VARCHAR(8), pass.LastRawEvent, 108) AS NVARCHAR(20))
|
||||
ELSE N'Нет выхода'
|
||||
END AS [Конец_дня],
|
||||
CASE
|
||||
WHEN pass.EmployeeID IS NOT NULL AND (pass.FirstIn IS NOT NULL OR pass.FirstRawEvent IS NOT NULL) THEN
|
||||
RIGHT('0' + CAST(DATEDIFF(MINUTE,
|
||||
ISNULL(pass.FirstIn, pass.FirstRawEvent),
|
||||
CASE
|
||||
WHEN @TargetDate = CAST(GETDATE() AS DATE) AND (pass.LastEventType = 'IN' OR pass.LastOut IS NULL OR pass.LastOut <= pass.FirstIn) THEN GETDATE()
|
||||
ELSE ISNULL(pass.LastOut, pass.LastRawEvent)
|
||||
END) / 60 AS VARCHAR), 2) + ':' +
|
||||
RIGHT('0' + CAST(DATEDIFF(MINUTE,
|
||||
ISNULL(pass.FirstIn, pass.FirstRawEvent),
|
||||
CASE
|
||||
WHEN @TargetDate = CAST(GETDATE() AS DATE) AND (pass.LastEventType = 'IN' OR pass.LastOut IS NULL OR pass.LastOut <= pass.FirstIn) THEN GETDATE()
|
||||
ELSE ISNULL(pass.LastOut, pass.LastRawEvent)
|
||||
END) % 60 AS VARCHAR), 2)
|
||||
ELSE N'00:00'
|
||||
END AS [Находился_в_здании],
|
||||
CASE
|
||||
WHEN pass.EmployeeID IS NOT NULL THEN N'Присутствовал'
|
||||
ELSE N'Отсутствовал (Нет событий)'
|
||||
END AS [Статус]
|
||||
FROM pList p WITH (NOLOCK)
|
||||
LEFT JOIN PDivision div WITH (NOLOCK) ON p.Section = div.ID
|
||||
LEFT JOIN PPost post WITH (NOLOCK) ON p.Post = post.ID
|
||||
LEFT JOIN Passages pass ON p.ID = pass.EmployeeID
|
||||
WHERE
|
||||
ISNULL(p.StatusRecord, 0) = 0
|
||||
AND p.DateTimeInArchive IS NULL
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT LIKE N'Аренд%'
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT IN (N'Без подразделения', N'')
|
||||
AND p.Name NOT LIKE N'бр.%'
|
||||
AND p.Name NOT LIKE N'Гость%'
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT IN (N'БГИ', N'КНР')
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT LIKE N'Рабоч%'
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT LIKE N'Врем%'
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT LIKE N'Практика%'
|
||||
AND ISNULL(CAST(div.Name AS NVARCHAR(255)), N'') NOT LIKE N'тест%'
|
||||
AND ISNULL(CAST(post.Name AS NVARCHAR(255)), N'') NOT LIKE N'Практикант%'
|
||||
ORDER BY p.Name ASC;
|
||||
"""
|
||||
Reference in New Issue
Block a user