generate_robot_report
Generate operational reports for Universal Robots by specifying robot ID, time range, and output path to document performance data.
Instructions
生成机器人运行报告
参数:
- robot_id: 机器人ID
- start_time: 开始时间戳
- end_time: 结束时间戳
- report_path: 报告保存路径
返回:
- 报告生成结果
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| robot_id | Yes | ||
| start_time | No | ||
| end_time | No | ||
| report_path | No |
Implementation Reference
- The MCP tool handler function decorated with @mcp.tool(), implementing generate_robot_report by calling advanced_data_analyzer.generate_report and handling errors.@mcp.tool() def generate_robot_report(robot_id: str, start_time: float = None, end_time: float = None, report_path: str = None): """ 生成机器人运行报告 参数: - robot_id: 机器人ID - start_time: 开始时间戳 - end_time: 结束时间戳 - report_path: 报告保存路径 返回: - 报告生成结果 """ try: if advanced_data_analyzer is None: return return_msg("高级数据分析器未初始化") # 生成报告 report = advanced_data_analyzer.generate_report( robot_id=robot_id, start_time=start_time, end_time=end_time, report_path=report_path ) if 'error' in report: return return_msg({"error": report['error']}) return return_msg({"success": True, "report": report}) except Exception as e: logger.error(f"生成机器人报告失败: {str(e)}") return return_msg(f"生成机器人报告失败: {str(e)}")
- The core helper method in AdvancedDataAnalyzer class that generates the comprehensive robot report by loading data, performing multiple analyses (statistical, performance, energy, anomaly, correlation), compiling results into a JSON report, and optionally saving to file.def generate_report(self, start_time: Optional[float] = None, end_time: Optional[float] = None, robot_id: Optional[str] = None, report_path: Optional[str] = None) -> Dict[str, Any]: """ 生成综合分析报告 Args: start_time: 开始时间 end_time: 结束时间 robot_id: 机器人ID report_path: 报告保存路径 Returns: Dict[str, Any]: 报告内容 """ # 加载数据 df = self.load_data( start_time=start_time, end_time=end_time, robot_id=robot_id ) if df.empty: return {'error': '没有找到数据'} report = { 'metadata': { 'generated_at': time.time(), 'generated_at_str': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'robot_id': robot_id, 'time_range': { 'start': start_time, 'end': end_time }, 'data_points': len(df) }, 'analyses': {} } # 执行各种分析 # 1. 统计分析 numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns.tolist() if numeric_cols: report['analyses']['statistical'] = self.analyze( df, AnalysisType.STATISTICAL, {'columns': numeric_cols[:5]} # 分析前5个数值列 ) # 2. 性能分析（如果有相关列） if 'operation_type' in df.columns and 'execution_time' in df.columns: report['analyses']['performance'] = self.analyze( df, AnalysisType.PERFORMANCE, {'operation_column': 'operation_type', 'duration_column': 'execution_time'} ) # 3. 能耗分析（如果有相关列） if 'voltage' in df.columns and 'current' in df.columns: report['analyses']['energy'] = self.analyze( df, AnalysisType.ENERGY, {'voltage_column': 'voltage', 'current_column': 'current'} ) # 4. 异常检测（对温度等关键参数） temperature_cols = [col for col in df.columns if 'temp' in col.lower() or 'temperature' in col.lower()] if temperature_cols: report['analyses']['anomalies'] = self.analyze( df, AnalysisType.ANOMALY, {'columns': temperature_cols} ) # 5. 相关性分析 report['analyses']['correlation'] = self.analyze( df, AnalysisType.CORRELATION ) # 保存报告 if report_path: os.makedirs(os.path.dirname(report_path), exist_ok=True) with open(report_path, 'w', encoding='utf-8') as f: json.dump(report, f, ensure_ascii=False, indent=2) logger.info(f"分析报告已保存到: {report_path}") return report