perf: simulation acceleration - filter non-social entities + configurable semaphore

- Filter out non-social entity types (API, SDK, Database, etc.) from agent configs
  reduces agent count ~20%, fewer LLM calls per round
- Make semaphore configurable via SIMULATION_SEMAPHORE env var (default 50, up from 30)
  allows higher concurrent LLM requests for faster round execution
- Reddit semaphore also reads from env var
- Estimated speedup: ~2x (25min → ~12min for 40 rounds)

backend/app/services/simulation_config_generator.py

@@ -293,7 +293,24 @@ class SimulationConfigGenerator:
         )
         
         reasoning_parts = []
-        
+
+        # ========== 预过滤：排除非社交实体 ==========
+        NON_SOCIAL_ENTITY_TYPES = {
+            'API', 'SDK', 'Database', 'WebServer', 'CloudService', 'CloudProvider',
+            'Table', 'Module', 'ManagementBackend', 'SecurityMeasure', 'Condition',
+            'PaymentTime', 'RiskLevel', 'CurrentStatus', 'Order', 'OrderDetail',
+            'EnrollmentInfo', 'Software', 'Technology', 'ProcessManager', 'WebPage',
+        }
+        original_count = len(entities)
+        entities = [e for e in entities
+                    if e.entity_type not in NON_SOCIAL_ENTITY_TYPES
+                    and not any(kw in e.name for kw in ['GET /', 'POST /', 'PUT /', 'DELETE /', 'HTTPS API', 'JSAPI', 'INDEX(', 'Bearer token', 'Header（', 'Hero（'])]
+        filtered_count = original_count - len(entities)
+        if filtered_count > 0:
+            logger.info(f"过滤非社交实体: {original_count} → {len(entities)} (排除 {filtered_count} 个技术/API实体)")
+            num_batches = math.ceil(len(entities) / self.AGENTS_PER_BATCH)
+            total_steps = 3 + num_batches
+
         # ========== 步骤1: 生成时间配置 ==========
         report_progress(1, t('progress.generatingTimeConfig'))
         num_entities = len(entities)

backend/scripts/run_parallel_simulation.py

@@ -1132,6 +1132,7 @@ async def run_twitter_simulation(
     
     # Twitter 使用通用 LLM 配置
     model = create_model(config, use_boost=False)
+    simulation_semaphore = int(os.environ.get("SIMULATION_SEMAPHORE", "50"))
     
     # OASIS Twitter使用CSV格式
     profile_path = os.path.join(simulation_dir, "twitter_profiles.csv")
@@ -1160,7 +1161,7 @@ async def run_twitter_simulation(
         agent_graph=result.agent_graph,
         platform=oasis.DefaultPlatformType.TWITTER,
         database_path=db_path,
-        semaphore=30,  # 限制最大并发 LLM 请求数，防止 API 过载（GLM RPM 友好值）
+        semaphore=simulation_semaphore,  # 可通过 SIMULATION_SEMAPHORE 环境变量调整
     )
     
     await result.env.reset()
@@ -1321,9 +1322,10 @@ async def run_reddit_simulation(
         print(f"[Reddit] {msg}")
     
     log_info("初始化...")
-    
+
     # Reddit 使用加速 LLM 配置（如果有的话，否则回退到通用配置）
     model = create_model(config, use_boost=True)
+    reddit_semaphore = int(os.environ.get("SIMULATION_SEMAPHORE", "50"))
     
     profile_path = os.path.join(simulation_dir, "reddit_profiles.json")
     if not os.path.exists(profile_path):
@@ -1351,7 +1353,7 @@ async def run_reddit_simulation(
         agent_graph=result.agent_graph,
         platform=oasis.DefaultPlatformType.REDDIT,
         database_path=db_path,
-        semaphore=30,  # 限制最大并发 LLM 请求数，防止 API 过载（GLM RPM 友好值）
+        semaphore=reddit_semaphore,  # 可通过 SIMULATION_SEMAPHORE 环境变量调整
     )
     
     await result.env.reset()