Variance value of conductivity.

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.水质指标（包括温度、pH值、浊度、电导率、总溶解固体（Total Dissolved Solids，TDS））的检测均采用五种国家标准方法。基于化学原理的检测技术可能会产生液体残留，引发二次污染。本水质监测与数据分析系统可有效解决传统检测方法需多台设备配合、需多次重复测量的问题。本文分析了特定时段内五台传感器的历史数据分布特征，实现数据的实时可视化展示，并提供水质超标预警功能；本文选取漓江不同河段的四份水样，依据国家标准方法开展检测，结果显示温度、pH值、总溶解固体（TDS）、电导率及浊度的平均测量误差分别为0.98%、2.23%、2.92%、3.05%及3.98%。本文进一步采用四分位法对超十万条的异常数据进行分析，并选取五个历史时段展开研究。实验结果表明，该系统在检测温度、pH值及总溶解固体（TDS）时稳定性较强，异常值占比分别为0.42%、0.84%及1.24%；在检测浊度与电导率时，异常值占比分别为3.11%与2.92%。在七种异常值填补方法的对比实验中，K近邻（K-Nearest Neighbor，KNN）算法的表现优于其余方法。对数据趋势、异常值、均值及极值的分析可辅助开展相关决策，包括设备更新与维护、应对极端水质状况、强化区域水质监管等。