nUR MCP Server

Instructions

分析机器人数据

参数:
- robot_id: 机器人ID
- analysis_type: 分析类型，可选值包括"statistical", "trend", "anomaly", "performance"
- start_time: 开始时间戳
- end_time: 结束时间戳

返回:
- 分析结果

Implementation Reference

• src/nonead_universal_robots_mcp/server.py:1091-1147 (handler)

  The handler function for the analyze_robot_data tool. Decorated with @mcp.tool() which registers it with the FastMCP server. Implements the core logic by loading robot data and performing analysis (statistical, trend, anomaly, performance) using the advanced_data_analyzer module.

  @mcp.tool()
  def analyze_robot_data(robot_id: str, analysis_type: str, start_time: float = None, end_time: float = None):
      """
      分析机器人数据
      
      参数:
      - robot_id: 机器人ID
      - analysis_type: 分析类型，可选值包括"statistical", "trend", "anomaly", "performance"
      - start_time: 开始时间戳
      - end_time: 结束时间戳
      
      返回:
      - 分析结果
      """
      try:
          if advanced_data_analyzer is None:
              return return_msg("高级数据分析器未初始化")
          
          # 映射分析类型字符串到枚举值
          type_map = {
              "statistical": AnalysisType.STATISTICAL,
              "trend": AnalysisType.TREND,
              "anomaly": AnalysisType.ANOMALY,
              "performance": AnalysisType.PERFORMANCE
          }
          
          if analysis_type not in type_map:
              return return_msg(f"不支持的分析类型: {analysis_type}")
          
          # 加载数据
          df = advanced_data_analyzer.load_data(
              robot_id=robot_id,
              start_time=start_time,
              end_time=end_time
          )
          
          if df.empty:
              return return_msg({"error": "未找到数据"})
          
          # 执行分析
          analysis_params = {}
          if analysis_type == "trend" and 'timestamp' in df.columns:
              # 对于趋势分析，使用时间戳作为x轴
              numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
              if numeric_columns and numeric_columns[0] != 'timestamp':
                  analysis_params = {'x_column': 'timestamp', 'y_column': numeric_columns[0]}
          
          result = advanced_data_analyzer.analyze(
              df,
              type_map[analysis_type],
              analysis_params
          )
          
          return return_msg({"analysis_type": analysis_type, "result": result})
      except Exception as e:
          logger.error(f"分析机器人数据失败: {str(e)}")
          return return_msg(f"分析机器人数据失败: {str(e)}")