feat(memory): add protected memory config deletion with end-user safeguards

- Add force parameter to delete_config endpoint for controlled deletion of in-use configs
- Implement MemoryConfigService.delete_config with protection against deleting default configs
- Add validation to prevent deletion of configs with connected end-users unless force=True
- Reorganize controller imports to remove duplicates and improve maintainability
- Clean up unused database connection management code from memory_storage_controller
- Add detailed docstring to delete_config endpoint explaining protection mechanisms
- Update error handling with specific BizCode.RESOURCE_IN_USE for configs in active use
- Add comprehensive logging for deletion attempts, warnings, and affected users
- Refactor ConfigParamsDelete schema usage to use MemoryConfigService directly
- Improve API response structure with affected_users count and force_required flag

api/app/core/error_codes.py

@@ -46,6 +46,7 @@ class BizCode(IntEnum):
     RESOURCE_ALREADY_EXISTS = 5002
     VERSION_ALREADY_EXISTS = 5003
     STATE_CONFLICT = 5004
+    RESOURCE_IN_USE = 5005
 
     # 应用发布（6xxx）
     PUBLISH_FAILED = 6001
@@ -125,6 +126,7 @@ HTTP_MAPPING = {
     BizCode.RESOURCE_ALREADY_EXISTS: 409,
     BizCode.VERSION_ALREADY_EXISTS: 409,
     BizCode.STATE_CONFLICT: 409,
+    BizCode.RESOURCE_IN_USE: 409,
     BizCode.PUBLISH_FAILED: 500,
     BizCode.NO_DRAFT_TO_PUBLISH: 400,
     BizCode.ROLLBACK_TARGET_NOT_FOUND: 400,

api/app/core/memory/analytics/implicit_memory/analyzers/dimension_analyzer.py

@@ -108,7 +108,6 @@ class DimensionAnalyzer:
             
             # Create dimension portrait
             portrait = DimensionPortrait(
-                user_id=user_id,
                 creativity=dimension_scores["creativity"],
                 aesthetic=dimension_scores["aesthetic"],
                 technology=dimension_scores["technology"],
@@ -220,7 +219,7 @@ class DimensionAnalyzer:
         """Create an empty dimension portrait when no data is available.
         
         Args:
-            user_id: Target user ID
+            user_id: Target user ID (used for logging only)
             
         Returns:
             Empty DimensionPortrait
@@ -228,7 +227,6 @@ class DimensionAnalyzer:
         current_time = datetime.now()
         
         return DimensionPortrait(
-            user_id=user_id,
             creativity=self._create_default_dimension_score("creativity"),
             aesthetic=self._create_default_dimension_score("aesthetic"),
             technology=self._create_default_dimension_score("technology"),

api/app/core/memory/analytics/implicit_memory/analyzers/interest_analyzer.py

@@ -7,7 +7,7 @@ providing percentage distribution that totals 100%.
 
 import logging
 from datetime import datetime
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional
 
 from app.core.memory.analytics.implicit_memory.llm_client import ImplicitMemoryLLMClient
 from app.core.memory.llm_tools.llm_client import LLMClientException
@@ -133,7 +133,6 @@ class InterestAnalyzer:
             
             # Create interest area distribution
             distribution = InterestAreaDistribution(
-                user_id=user_id,
                 tech=interest_categories["tech"],
                 lifestyle=interest_categories["lifestyle"],
                 music=interest_categories["music"],
@@ -251,7 +250,7 @@ class InterestAnalyzer:
         """Create an empty interest distribution when no data is available.
         
         Args:
-            user_id: Target user ID
+            user_id: Target user ID (used for logging only)
             
         Returns:
             Empty InterestAreaDistribution with equal percentages
@@ -259,15 +258,15 @@ class InterestAnalyzer:
         current_time = datetime.now()
         equal_percentage = 25.0  # 100% / 4 categories
         
-        default_category = lambda name: InterestCategory(
-            category_name=name,
-            percentage=equal_percentage,
-            evidence=["Insufficient data for analysis"],
-            trending_direction=None
-        )
+        def default_category(name: str) -> InterestCategory:
+            return InterestCategory(
+                category_name=name,
+                percentage=equal_percentage,
+                evidence=["Insufficient data for analysis"],
+                trending_direction=None
+            )
         
         return InterestAreaDistribution(
-            user_id=user_id,
             tech=default_category("tech"),
             lifestyle=default_category("lifestyle"),
             music=default_category("music"),