feat(1c): add read-only companies export layer

clickhouse-1c/ai/company_intelligence_api.py

@@ -59,6 +59,7 @@ def health() -> dict[str, Any]:
             """
             SELECT
                 countIf(counterparty != '') AS documents_with_counterparty,
+                (SELECT count() FROM analytics_1c.companies) AS companies_total,
                 (SELECT count() FROM analytics_1c.company_forecasts) AS forecasts_total,
                 (SELECT count() FROM analytics_1c.company_health_signals) AS health_signals_total
             FROM analytics_1c.documents
@@ -83,6 +84,14 @@ def companies_overview(
         infobase,
         organization,
         counterparty,
+        company_name,
+        owner_user,
+        base_path,
+        current_status,
+        db_size_bytes,
+        reglog_size_bytes,
+        active_locks,
+        current_activity_score,
         last_seen_at,
         days_since_last_activity,
         docs_7d,
@@ -135,6 +144,16 @@ def company_summary(counterparty: str, infobase: str | None = None) -> dict[str,
     ORDER BY score DESC, generated_at DESC
     """
     timeline_sql = f"""
+    SELECT ts, infobase, company_name, owner_user, current_status, db_size_bytes, reglog_size_bytes, active_locks, current_activity_score
+    FROM analytics_1c.v_company_portfolio_overview
+    WHERE counterparty = {q(counterparty)}
+    {"AND infobase = " + q(infobase) if infobase else ""}
+    LIMIT 1
+    """
+    forecasts = rows_to_dict(client.query(forecast_sql))
+    signals = rows_to_dict(client.query(signals_sql))
+    company_state = rows_to_dict(client.query(timeline_sql))
+    timeline_sql = f"""
     SELECT ts, infobase, doc_type, operation_type, amount, status, author
     FROM analytics_1c.documents
     WHERE counterparty = {q(counterparty)}
@@ -142,8 +161,6 @@ def company_summary(counterparty: str, infobase: str | None = None) -> dict[str,
     ORDER BY ts DESC
     LIMIT 20
     """
-    forecasts = rows_to_dict(client.query(forecast_sql))
-    signals = rows_to_dict(client.query(signals_sql))
     timeline = rows_to_dict(client.query(timeline_sql))
     essence = (
         f"Компания {counterparty}: за 30 дней событий {card['docs_30d']}, суммарная активность {card['amount_30d']}, "
@@ -152,6 +169,7 @@ def company_summary(counterparty: str, infobase: str | None = None) -> dict[str,
     return {
         "essence": essence,
         "card": card,
+        "company_state": company_state[0] if company_state else None,
         "forecasts": forecasts,
         "signals": signals,
         "recent_documents": timeline,

clickhouse-1c/ai/refresh_company_intelligence.py

@@ -164,6 +164,22 @@ def main() -> int:
             """,
         )
     }
+    company_state_map = {
+        row["infobase"]: row
+        for row in query_rows(
+            client,
+            """
+            SELECT
+                infobase,
+                current_status,
+                active_locks,
+                temp_db_present,
+                scheduler_touched,
+                current_activity_score
+            FROM analytics_1c.v_companies_current
+            """,
+        )
+    }
 
     forecast_rows: list[list[Any]] = []
     signal_rows: list[list[Any]] = []
@@ -186,6 +202,12 @@ def main() -> int:
         days_since_last_activity = (date.today() - latest_day).days
         open_cases_total = cases_map.get((infobase, counterparty), 0)
         detections_total = detections_map.get((infobase, counterparty), 0)
+        company_state = company_state_map.get(infobase, {})
+        current_status = str(company_state.get("current_status") or "")
+        active_locks = int(company_state.get("active_locks") or 0)
+        temp_db_present = int(company_state.get("temp_db_present") or 0)
+        scheduler_touched = int(company_state.get("scheduler_touched") or 0)
+        current_activity_score = float(company_state.get("current_activity_score") or 0)
 
         for metric, values in (("docs_total", docs_series), ("amount_total", amount_series)):
             for horizon in horizons:
@@ -221,6 +243,11 @@ def main() -> int:
             signals.append(("amount_drop", 70, "high", f"Активность по компании {counterparty} упала более чем на 50% неделя к неделе."))
         if docs_prev_7d > 0 and docs_7d == 0:
             signals.append(("docs_stopped", 55, "medium", f"По компании {counterparty} прекратился поток документов за последние 7 дней."))
+        if current_status == "busy" or active_locks > 0 or temp_db_present > 0:
+            score = min(85, 45 + active_locks * 5 + temp_db_present * 10)
+            signals.append(("base_busy", score, severity_score_to_label(score), f"Файловая база компании {counterparty} занята: status={current_status}, locks={active_locks}, tempDb={temp_db_present}."))
+        if scheduler_touched > 0 and current_activity_score >= 15:
+            signals.append(("scheduler_activity", 35, "medium", f"По компании {counterparty} есть активность scheduler и повышенный activity score {current_activity_score}."))
         if open_cases_total > 0:
             signals.append(("open_cases", min(95, 40 + open_cases_total * 10), severity_score_to_label(min(95, 40 + open_cases_total * 10)), f"По компании {counterparty} есть открытые кейсы: {open_cases_total}."))
         if detections_total > 0: