水质监测与异常预测数据集

水质监测与异常预测数据集是通过安装在逐鹿县污水进口、中水出口的水流量计量设备和水质量自动检测设备采集的。将采集到的水质pH值、溶解氧、COD、氨氮等理化指标，通过数采仪上传至张家口市污染源自动监控系统，再对比进水水质数据与《污水排入城镇下水道水质标准》（GB/T 31962-2015），判断污水是否超标、是否需预处理；利用采集到的数据集历史数据预测未来24-72小时进水污染物浓度变化，识别出工业区突发高浓度废水排放等风险。能广泛应用于于城市饮用水水源地、工业园区排污口、农业面源污染区等重点管控区域的水质分析。解决了从"人工采样+实验室检测"的滞后性分析，转变为实时在线监测与提前预警，将污染事件响应时间从"天级"缩短至"小时级"。挖掘水质指标与影响因素的关联规律，提升异常预警准确率，降低误报率。

The water quality monitoring and anomaly prediction dataset is collected via water flow metering equipment and automatic water quality testing devices installed at the sewage inlet and reclaimed water outlet of Zhuolu County. The collected physicochemical indicators including water pH value, dissolved oxygen, COD, ammonia nitrogen and others are uploaded to the Zhangjiakou Automatic Pollution Source Monitoring System via data acquisition instruments. Then, by comparing the influent water quality data with the Standard for Quality of Wastewater Discharged into Urban Sewers (GB/T 31962-2015), it is determined whether the wastewater exceeds the standard and whether pretreatment is required. Using the historical data of the collected dataset, it predicts the changes in influent pollutant concentration over the next 24 to 72 hours, and identifies risks such as sudden high-concentration wastewater discharge in industrial zones. It can be widely applied to water quality analysis in key controlled areas such as urban drinking water source areas, sewage outlets of industrial parks, and agricultural non-point source pollution zones. It solves the lagged analysis mode of "manual sampling + laboratory testing", shifts to real-time online monitoring and early warning, and shortens the response time for pollution incidents from "day-level" to "hour-level". It excavates the correlation rules between water quality indicators and their influencing factors, improves the accuracy of anomaly early warning, and reduces the false alarm rate.

搜集汇总

背景与挑战

背景概述

该数据集专注于水质监测领域，可能包含如pH值、溶解氧、浊度等关键水质参数的时序数据，用于支持异常检测和预测模型的开发。其特点在于结合环境监测与机器学习应用，旨在帮助识别水质变化趋势和潜在风险，适用于环境科学、公共健康或工业管理场景。