Fix Groq API compatibility in parallel simulation

- Patch camel tool schema to add empty properties when required is
  present but properties is missing (fixes do_nothing tool rejection)
- Fix LLM-generated concatenated hour arrays in config loading
  (e.g. [19202122] -> [19,20,21,22])

backend/scripts/run_parallel_simulation.py

@@ -168,6 +168,23 @@ try:
         generate_twitter_agent_graph,
         generate_reddit_agent_graph
     )
+
+    # Patch camel tool schema for Groq compatibility.
+    # Groq rejects tool schemas where 'required' is present but 'properties'
+    # is missing (e.g. zero-parameter tools like do_nothing).
+    import camel.toolkits.function_tool as _ft
+    _original_get_openai_tool_schema = _ft.get_openai_tool_schema
+
+    def _patched_get_openai_tool_schema(func):
+        schema = _original_get_openai_tool_schema(func)
+        params = schema.get("function", {}).get("parameters", {})
+        if "required" in params and "properties" not in params:
+            params["properties"] = {}
+        if not params.get("properties") and "required" in params:
+            del params["required"]
+        return schema
+
+    _ft.get_openai_tool_schema = _patched_get_openai_tool_schema
 except ImportError as e:
     print(f"错误: 缺少依赖 {e}")
     print("请先安装: pip install oasis-ai camel-ai")
@@ -601,10 +618,51 @@ class ParallelIPCHandler:
             return True
 
 
+def _fix_hour_array(val):
+    """Fix LLM-generated concatenated hour arrays.
+
+    Some models produce [19202122] instead of [19,20,21,22] or
+    ["012345"] instead of [0,1,2,3,4,5]. Parse these back into
+    individual hour integers (0-23).
+    """
+    if not isinstance(val, list) or len(val) != 1:
+        return val
+    item = val[0]
+    if isinstance(item, str):
+        return [int(ch) for ch in item if ch.isdigit()]
+    if isinstance(item, int) and item > 23:
+        s = str(item)
+        hours = []
+        i = 0
+        while i < len(s):
+            if i + 1 < len(s):
+                two_digit = int(s[i:i + 2])
+                if 10 <= two_digit <= 23:
+                    hours.append(two_digit)
+                    i += 2
+                    continue
+            hours.append(int(s[i]))
+            i += 1
+        return hours
+    return val
+
+
 def load_config(config_path: str) -> Dict[str, Any]:
     """加载配置文件"""
     with open(config_path, 'r', encoding='utf-8') as f:
-        return json.load(f)
+        config = json.load(f)
+
+    # Fix malformed hour arrays from LLM config generation
+    time_config = config.get("time_config", {})
+    for key in ("peak_hours", "off_peak_hours"):
+        if key in time_config:
+            time_config[key] = _fix_hour_array(time_config[key])
+
+    for agent_cfg in config.get("agent_configs", []):
+        if "active_hours" in agent_cfg:
+            agent_cfg["active_hours"] = _fix_hour_array(agent_cfg["active_hours"])
+
+    return config
 
 
 # 需要过滤掉的非核心动作类型（这些动作对分析价值较低）