如何提升自动监测异常数据分析研判的精准性？

Group 1 - The article highlights the inefficiency of current automatic monitoring data analysis in environmental enforcement, with over 90% of identified pollution cases being inaccurate [1][2] - A significant portion of false positives in pollution data is attributed to equipment malfunctions and maintenance issues, rather than actual emissions [1][2] - The reliance on automatic monitoring data is intended to reduce on-site inspections, but inadequate analysis capabilities have led to an increase in unnecessary inspections [2][3] Group 2 - There is an urgent need to establish a scientific and reasonable mechanism for analyzing abnormal automatic monitoring data, moving beyond simplistic standards [3] - Shanghai's recent regulations emphasize the specialization of responsibility in data analysis, allowing monitoring agencies to identify and report suspicious data [3] - The division of responsibilities between monitoring and enforcement agencies has improved the reliability of pollution data analysis [3] Group 3 - The integration of big data and artificial intelligence can enhance the precision and efficiency of data analysis in environmental monitoring [4] - An example from Suzhou demonstrates the effectiveness of an AI system that analyzes various characteristics of abnormal data, leading to high accuracy in identifying violations [4] - Developing localized models for automatic monitoring data can filter out ineffective data and quickly identify potential violations, improving overall analysis accuracy [4]